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How a Chief Procurement Officer sees translation

Source: Slator
Story flagged by: Jared Tabor

Understanding the buyer and the variables they consider in procurement is essential to securing and retaining strategic accounts. At the inaugural SlatorCon London on May 9, 2017, over 60 senior industry executives were treated to a snapshot of how the procurement brain works by one of the language industry’s largest buyers.

With USD 7.8bn in revenue and 50,000 professionals serving clients in over 100 markets, QuintilesIMS is by far the world’s largest clinical research organization. It purchases millions of dollars in translation for itself and its clients. The company is said to be one of TransPerfect’s major global accounts.

At SlatorCon, Steve Kirk, Chief Global Procurement Officer at QuintilesIMS, discussed their strategic sourcing process, what category management looks like, centralized vs. decentralized procurement models, and where they place translation in their procurement mix.

He walked participants through the many variables they consider when evaluating a supplier relationship with a Language Service Provider (LSP) and challenged the audience to know how many of these variables are important to both you and the buyer.

Kirk works with his teams in North America, Europe, EMEA and Asia Pacific to manage global and local purchasing and he detailed the process they take to identify, segment and manage Strategic Sourcing. For QuintilesIMS, the five steps are Initiation, Insight, Innovation, Implementation and Improvement.

“Everyone has a slightly different version of the five, six or seven steps that you go through but the flow is pretty much the same,” said Kirk. “Go off, get your data, work out what your category looks like, and try to get together some kind of a business case of what you’re trying to achieve from the category.”

Focusing in on the translation category, Kirk highlighted the challenges they face in building a team. “You’re looking at trying to get together people that are maybe in dispersed geographies, different parts of the business,” said Kirk. “They might be doing marketing translation, they might be doing clinical translation, they might be looking at something that is part of our market research division in the IMS case. [We are] trying to work out how you can get all those different guys together into one room and say right, how do we build a spec here?”

Adding to the complexity for translation is QuintilesIMS’ “fragmented spend across the business.” Kirk said, “You’ve got a fragmented industry, with a fragmented supply base, with a fragmented nature of spend in translation. How do you get your hands around that and work out what you’re really buying?”

“You’re looking at trying to get together people that are maybe in dispersed geographies, different parts of the business” — Steve Kirk, Chief Global Procurement Officer, QuintilesIMS

A large portion of QuintilesIMS’ translation spend is passed through to their clients, i.e. the world’s largest pharma companies, which expect them to be custodians of that spend on their behalf. As such, QuintilesIMS is constantly measuring and analyzing data as it seeks to improve supplier management, achieve economies of scale and leverage buying power.

Sourcing cycles are accelerating from the current 2-3 years, placing even more pressure on vendors. “What we’re driving towards now is a more frequent sourcing process. A more regular challenge to our supply base,” said Kirk. “And we’re going to try to increase our sourcing velocity,” indicating a potential target cycle as short as 12 to 18 months.

Within the supplier management process, QuintilesIMS are using globalization and centralization to build a global supply base and leverage their buying power.

“You typically start with 50, 60, or 100 suppliers doing translations,” said Kirk. “New procurement comes in, category management comes in, a new CPO comes in. Let’s try to use economies of scale, let’s try and bundle together our spend, let’s try to get to one single contract with one single supplier.”


An algorithm summarizes lengthy text surprisingly well

Source: MIT Technology Review
Story flagged by: Jared Tabor

An algorithm developed by researchers at Salesforce shows how computers may eventually take on the job of summarizing documents. It uses several machine-learning tricks to produce surprisingly coherent and accurate snippets of text from longer pieces. And while it isn’t yet as good as a person, it hints at how condensing text could eventually become automated.

The algorithm produced, for instance, the following summary of a recent New York Times article about Facebook trying to combat fake news ahead of the U.K.’s upcoming election:

  • Social network published a series of advertisements in newspapers in Britain on Monday.
  • It has removed tens of thousands of fake accounts in Britain.
  • It also said it would hire 3,000 more moderators, almost doubling the number of people worldwide who scan for inappropriate or offensive content.

The Salesforce algorithm is dramatically better than anything developed previously, according to a common software tool for measuring the accuracy of text summaries.

“I don’t think I’ve ever seen such a large improvement in any [natural-language-processing] task,” says Richard Socher, chief scientist at Salesforce. Socher is a prominent name in machine learning and natural-language processing, and his startup, MetaMind, was acquired by Salesforce in 2016.

The software is still a long way from matching a human’s ability to capture the essence of document text, and other summaries it produces are sloppier and less coherent. Indeed, summarizing text perfectly would require genuine intelligence, including commonsense knowledge and a mastery of language.


EContent magazine names Smartling a 2017 product trendsetter for the digital content industry

Source: Marketwired
Story flagged by: Jared Tabor

Smartling, a translation technology and service innovator, has announced it was named by EContent magazine as a 2017 product trendsetter for its Mobile Delivery Network translation platform.

The select list is a result of the magazine’s effort “to find out what products are helping content creators of all kinds stay on top of their game,” said EContent magazine Editor Theresa Cramer. “Whether it’s the booming podcast industry, the burgeoning market for virtual reality, or the proliferation of devices bringing content straight into the home, our editors have sought out the technologies driving the digital content industry’s growth.”

Smartling’s Mobile Delivery Network solves what traditionally has been a problematic interdependency between mobile app release cycles and translated content updates, enabling developers, translators, and localization professionals to work independently of each other. Updated translations and newly localized content are delivered to the app through this over-the-air service, which means multilingual content and translation edits can be released on a separate schedule, completely decoupled from updates to the app’s core code.


Iconic’s language tech creates first English version of world’s oldest chemical journal

Source: Slator
Story flagged by: Jared Tabor

Iconic Translation Machines (Iconic), a leading Machine Translation (MT) software and solutions provider is pleased to announce its involvement in the creation of ChemZent™, the first and only indexed and searchable English-language version of Chemisches Zentralblatt – the oldest compendium of German chemistry abstracts dating from 1830-1969.

Iconic partnered with CAS, a division of the American Chemical Society, to produce ChemZent. This new CAS solution provides immeasurable value to researchers and institutions worldwide by allowing users to access the entire Chemisches Zentralblatt collection in one place using SciFinder ®, searchable in English with indexing of relevant chemical substances and concepts for ease of discoverability.

Iconic enabled this solution by developing innovative machine learning technology to extend its existing machine translation and natural language processing solutions. Iconic’s unrivalled expertise together with CAS industry-leading scientific information analysis made the launch of ChemZent possible within one year of idea inception.

The process of creating ChemZent involved large scale digitisation and translation of 140 years’ worth of German chemical information – journals and patents – for indexing and search. Iconic digitised 800,000 image-based PDF documents via Optical Character Recognition (OCR).

It then extracted individual articles, separated them into fields by author and title, and machine translated them from German into English, before CAS indexed the records for search. On completion more than 3 million chemical abstracts and one billion words were translated across the entire Chemisches Zentralblatt collection.

Full case study available here:


New earbuds promise real-time translation

Source: Wired
Story flagged by: Jared Tabor

They look a bit more stylish than your average babel fish, but it remains to be seen if they work as well. From the article:

The earbuds run on a new version of Bragi’s operating system, which will come to the original Dash as well. It enables the simpler pairing process, helps the buds auto-detect a workout, and refines the on-bud touch controls, which until now were about as easy to learn as Morse Code. The new OS also introduces two of the more futuristic features Bragi’s been talking about for years: real-time translation, through a partnership with the iTranslate app, and a gesture interface that lets you control your music just by moving your head.

Even the regular, non-custom Dash Pros are a big upgrade over the previous model.


Dancing sign language interpreter raps with her hands interpreting for Snoop Dogg

Source: Laughing Squid
Story flagged by: Jared Tabor

Holly Manniaty Snoop Dogg NOLA Jazz Fest

An incredibly animated dancing sign language interpreter brilliantly rapped with her hands, capturing the lyrics and spirit of Snoop Dogg‘s performance at the 2017 New Orleans Jazz Fest. The interpreter, a Portland, Oregon resident named Holly Maniatty has provided her uniquely wonderful skills to a variety of famous acts including Bruce SpringsteenWu Tang Clan and many, many more. In 2014, Holly took part in Jimmy Kimmel’s “Sign Language Rap Battle with Wiz Khalifa“.

See video (explicit language) >>

“Freelance Isn’t Free” Act goes into effect in NYC

By: Susana Magnani

This Act will provide resources for freelancers to claim overdue payment from clients. Although applicable to all freelancers, I thought it would be of interest to our community.

… the law mandates that freelancers be paid in full for work worth $800 or more, either by a date set forward in writing or within 30 days of completing an assigned task.

Wouldn’t it be great to have this kind of protection everywhere else?
(Article by Emma Whitford on Gothamist)

Machines to Replace Human Translators? They Already Have: English Tabloid

By: Peter Gleason

[UPDATE 1530 CET 2017/05/15: The report in question has now been published by Adzuna (link) ]

CRACOW, Poland, May 15 —England’s Daily Mail apparently has an exclusive on the end of the Translation & Localization Industry as we know it. If the British ‘tabloid’ is to be believed, the end is not merely nigh, it’s already here: according to an admittedly ungooglable “study from jobs search engine Adzuna” of “79 million job adverts placed in Britain in the previous two years,” robots are already taking human translators’ jobs on a “grand scale,” and with blame/credit belonging primarily to “Google … among those to have designed automated translation software, which is making human translators increasingly redundant.”

The news also made it around the Commonwealth, being picked up this morning by the Australian, who also failed to link to or otherwise properly reference the ephemeral report. Nevertheless, it ominously quotes UK job site Adzuna co-founder Doug Monro as predicting, “Automation is already replacing jobs and could be set to replace some roles, like translators and travel agents, entirely.”


Increase in research of neural machine translation in the first half of 2017

Source: Slator
Story flagged by: Jared Tabor

Academia continues to ramp up its research into neural machine translation (NMT). Five months into the year, the number of papers published in the open-access science archive,, nearly equals the research output for the entire year 2016. The spike confirms a trend Slator reported in late 2016, when we pointed out how NMT steamrolls SMT.

As of May 7, 2017, the Cornell University-run had a total of 137 papers in its repository, which had NMT either in their titles or abstracts. From only seven documents published in 2014, output went up to 11 in 2015. But the breakthrough year was 2016, with research output hitting 67 contributions.

NMT, or an approach to machine translation based on neural networks, is seen as the next evolution after phrase-based statistical machine translation (SMT) and the previous rules-based approach.

While many studies and comparative evaluations have pointed to NMT’s advantages in achieving more fluent translations, the technology is still in its nascent stage and interesting developments in the research space continue to unfold.

At press time, NMT papers submitted in 2017 were authored by 173 researchers from across the world, majority of them (63 researchers) being affiliated with universities and research institutes in the US.

The most prolific contributor is Kyunghyun Cho, Assistant Professor at the Department of Computer Science, Courant Institute of Mathematical Sciences Center for Data Science, New York University. Cho logged 14 citations last year.

He has, so far, co-authored three papers this year —  “Nematus: a Toolkit for Neural Machine Translation,” “Learning to Parse and Translate Improves Neural Machine Translation,” and “Trainable Greedy Decoding for Neural Machine Translation” — in collaboration with researchers from the University of Edinburgh, Heidelberg University, and the University of Zurich in Europe; the University of Tokyo and the University of Hong Kong in Asia; and the Middle East Technical University in Turkey.

Aside from Cho, 62 other researchers with interest in NMT have published their work on arXiv under the auspices of eight American universities: UC Berkeley, Carnegie Mellon, NYU, MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Stanford, Georgia Institute of Technology Atlanta, Johns Hopkins University, and Harvard.

Sixty-one researchers from Europe have also substantially contributed to the collection, with authors from the UK (18), Germany (11), Ireland (13), and the Netherlands (7) submitting the most papers.


Best Translated Book Awards 2017 winners announced

Source: Three Percent
Story flagged by: Jared Tabor

The tenth annual Best Translated Book Awards were announced on May 5th, with Lúcio Cardoso’s Chronicle of the Murdered House, translated from the Portuguese by Margaret Jull Costa and Robin Patterson, winning for fiction, and Alejandra Pizarnik’s Extracting the Stone of Madness,translated by Yvette Siegert, winning for poetry.

See more >>

Facebook to open source its neural machine translation

Source: Slator
Story flagged by: Jared Tabor

Facebook is claiming that a new approach to machine translation using convolutional neural networks (CNNs) can help translate languages more accurately (read: increase quality on a BLEU scale) and up to nine times faster than the traditional recurrent neural networks (RNNs). CEO Mark Zuckerberg himself announced the news in his own Facebook page.

The company’s bold claims were anchored on results of a study conducted by five members of Facebook’s Artificial Intelligence Research (FAIR) team and outlined in detail in a paper entitled “Convolutional Sequence to Sequence Learning.” “To help us get there faster, we’re sharing our work publicly so that all researchers can use it to build better translation tools,” Zuckerberg said. Research authors Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin shared in an accompanying post on the Facebook developer blog that the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub.

Dr. John Tinsley, CEO & Co-Founder, Iconic Translation Machines Ltd., who reviewed the paper, told Slator that the results are impressive. “It’s quite a different approach, using convolutional neural networks (CNNs) as opposed to recurrent neural networks (RNNs). The reason this hasn’t been looked at for translation before is that CNNs typically work well with fixed-length input and RNNs with variable-length input. Obviously, with language, things are very variable so RNNs were the natural starting point,” he explained. His concern, though, is quality. But he observed that some of the shared task data reported are comparable to if perhaps a little better than existing approaches to Neural MT. “However, the single biggest impact of this work is the speed,” he said. “One of the current drawbacks of Neural MT is how long it actually takes to train the models, and this approach by Facebook using CNNs allows them to be trained up to seven times faster. This is because it’s much easier to parallelise the training process of CNNs given how they process different parts of the data (simultaneously as opposed to sequentially). That being said, it still requires powerful hardware.”

On the release of the source code, Dr. Tinsley said he approves of the open source approach. He said, “It’s good to see the tech behemoths taking this approach now and opening up their research to the wider community.”


Translators without Borders improves and expands its translation platform

Source: Translators without Borders
Story flagged by: Jared Tabor
Kató is the improved and expanded translation platform, formerly known as the Translators without Borders (TWB) Workspace, and it is where much of the magic happens. Kató connects over 500 non-profit partners with a diverse community of volunteer translators and many other language services. First established as the TWB Workspace in 2011 following a collaboration between TWB and, the online platform has since helped non-profit partners such as Doctors without Borders, Refugee Aid and Save the Children to share essential information in local languages and to translate over 40 million words. Today, the revamped Kató is more robust than ever with computer-assisted translation tools, functionality for storing common words and taxonomies and even bigger incentives for the community. Translators can now use Kató to interpret for all media, including providing subtitles and voiceovers for videos. The platform is even being used to train fluent speakers of languages that desperately need more translators and interpreters.


40+ million words translated so far

4,000 professional translators

500+ non-profit partners

190 language pairs

See more >>

Could machine translation help slow the decline of endangered languages?

Source: MIT Technology Review
Story flagged by: Jared Tabor

The best guess is that humans currently speak about 6,900 different languages. More than half the global population communicates using just a handful of them—Chinese, English, Hindi, Spanish, and Russian. Indeed, 95 percent of people communicate using just 100 languages.

The other argots are much less common. Indeed, linguists estimate that about a third of the world’s languages are spoken by fewer than 1,000 people and are in danger of dying out in the next 100 years or so. With them will go the unique cultural heritage that they embody—stories, phrases, jokes, herbal remedies, and even unique emotions.

It’s easy to think that machine learning can help. The problem is that machine translation relies on huge annotated data sets to ply its trade. These data sets consist of vast corpora of books, articles, and websites that have been manually translated into other languages. This acts like a Rosetta Stone for machine-learning algorithms, and the bigger the data set, the better they learn.

A map showing how the past tense indicators cluster for 100 of the languages investigated.

But these huge data sets simply do not exist for most languages.  That’s why machine translation works only for a tiny fraction of the most common lingos. Google Translate, for example, only speaks about 90 languages.

So an important challenge for linguists is to find a way to automatically analyze less common languages to better understand them.

Today, Ehsaneddin Asgari and Hinrich Schutze at Ludwig-Maximilian University of Munich in Germany say they have done just that. Their new approach reveals important elements of almost any language that can then be used as a stepping stone for machine translation.

The new technique is based around a single text that has been translated into at least 2,000 different languages. This is the Bible, and linguists have long recognized its importance in their discipline.

Consequently, they have created a database called the Parallel Bible Corpus, which consists of translations of the New Testament in 1,169 languages. This data set is not big enough for the kind of industrial machine learning that Google and others perform. So Asgari and Schutze have come up with another approach based on the way tenses appear in different languages.

Most languages use specific words or letter combinations to signify tenses. So the new trick is to manually identify these signals in several languages and then use data-mining techniques to hunt through other translations looking for words or strings of letters that play the same role.

For example, in English the present tense is signified by the word “is,” the future tense by the word “will,” and the past tense by the word “was.” Of course, there are other signifiers too.

Asgari and Schutze’s idea is to find all these words in the English translation of the Bible along with other examples from a handful other language translations. Then look for words or letters strings that play the same role in other languages. For example, the letter string “-ed” also signifies the past tense in English.

But there is a twist. Asgari and Schutze do not start with English because it is a relatively old language with many exceptions to the rule, which makes it hard to learn.

Instead, they start with a set of Creole languages that have developed from a mixture of other languages. Because they are younger, Creole languages have had less time to develop these linguistic idiosyncrasies. And that means they generally contain better markers of linguistic features such as tense. “Our rationale is that Creole languages are more regular than other languages because they are young and have not accumulated ‘historical baggage’ that may make computational analysis more difficult,” they say.


The language we speak affects our perception of time: research

Source: Times Live (NZ)
Story flagged by: Jared Tabor

New research has revealed another powerful effect that language can have on the brain, finding that the language we speak can influence the way we experience time.

Carried out by Professor Panos Athanasopoulos, a linguist from Lancaster University, UK, and Professor Emanuel Bylund, a linguist from Stellenbosch University and Stockholm University, Sweden, the study is the first to find evidence of cognitive flexibility in people who speak two languages.

Although bilinguals easily and quickly switch between their two languages without even thinking about it (a phenomenon called code-switching), it is not just a matter of switching to a different language for communication — different languages influence the way we think about the world around us.

The study found that people who speak two languages fluently think about time differently depending on the language they are using when estimating the duration of an event.

The research team explain that for example, Swedish and English speakers prefer to mark the duration of events by referring to a physical distance such as a short break, or a long wedding, with the passage of time perceived as distance traveled.

Greek and Spanish speakers on the other hand, prefer to mark the duration of events by referring to physical quantities, such as a small break, a big wedding, with the passage of time perceived as volume.

For the research the team recruited Spanish-Swedish bilinguals, whose two languages look at time differently.

Participants were asked to watch either a line growing across a screen, or a container being filled, and estimate how much time had passed.

At the same time as watching they were either prompted with the word ‘duración’ — Spanish for duration — or ‘tid’ — Swedish for duration.

The results showed a clear difference between the two languages.

When watching containers filling up and prompted in Spanish, participants based their time estimates on how full the containers were, not by the lines growing on screens, suggesting that they perceived time as volume.

However, when given participants were prompted in Swedish, participants based their time estimates on lines growing on screens, suggesting that they perceived time as distance traveled.

“By learning a new language, you suddenly become attuned to perceptual dimensions that you weren’t aware of before,” commented Professor Athanasopoulos.

“The fact that bilinguals go between these different ways of estimating time effortlessly and unconsciously fits in with a growing body of evidence demonstrating the ease with which language can creep into our most basic senses, including our emotions, our visual perception, and now it turns out, our sense of time.”

Slator’s reader poll results on payments, the busy season, and finding freelancers

Source: Slator
Story flagged by: Jared Tabor

Slator has published the results of a recent reader poll, on topics such as payments for translation services in the future, LSPs’ busiest quarters of the year, predictions on LSP stock performance, and where vendor managers go to find freelance translation professionals.

From the section “How to Find Freelancers”:

Professional freelance linguists are the lifeblood of the language services industry. While translation marketplaces may serve their purpose, every LSP worth its salt will see it fit to build and maintain a core pool of freelancers — experts in their field and intimately familiar with specific client requirements.

A number of large buyers have recently opted out of using LSPs entirely, choosing to work instead with freelance talent. The EU Court of Justice, for example, despite having 600 “lawyer-linguists” on staff, issued a EUR 6m freelancer-only tender at the start of the year. Even the European Central Bank awarded 10 freelancers a place in its EUR 2.4m contract.

So where do vendor managers go to find freelance translators? Trade shows, according to 38% of respondents in a recent Slator poll. Others find linguists via ProZ (21%) and referrals (18%). Associations (7%) posted about even numbers as universities (7%).

See the full set of results >>

smartCAT and Lilt announce partnership

Source: Slator
Story flagged by: Jared Tabor

Translation Management Software (TMS) provider, smartCAT and Lilt, an interactive and adaptive Machine Translation (MT) tool, have partnered to combine the latest in MT technology with a robust collaborative translation environment.

smartCAT, whose integrated approach to translation automation offers a broad range of human and machine translation solutions to their customers, are excited to be adding the latest MT technology. Their willingness to rapidly adopt new technology for the success of their customers made them a perfect candidate to take advantage of Lilt’s REST API. The technology enables programmatic integration with Lilt, as well as their Javascript library, lilt.js, which allows the addition of interactive, adaptive machine translation to a CAT editor.

Lilt’s adaptive MT is now available within the smartCAT editor, giving smartCAT’s customers access to this technology in a single activation click.

“Lilt fit just right into the smartCAT ecosystem. We loved the futuristic approach to machine translation it promotes, so delivering the technology to our users instantly became our priority. What makes this integration so special is that it takes smartCAT’s unique real-time multi-user collaboration to the next level. Each time a translator confirms a segment, the engine instantly trains and provides the correct suggestions to all the participants in the project, helping them to maintain consistency across the text. Despite the technology behind the new feature being complex and unfamiliar, we made sure it’s intuitive and easy to use.” said Ivan Smolnikov, CEO at smartCAT.

Lilt’s mission of making high-quality translation widely available led them to offer their REST API and Javascript library to translation solution developers, like smartCAT, in order to incorporate a powerful adaptive MT technology into their existing systems. Partnerships such as this one give customers and translators access to better translation quality, as well as a more ergonomic environment compared to the traditional methods of post-editing.


Top Language Lovers 2017 competition now open for nominations

Source: Lexiophiles
Story flagged by: Jared Tabor and Lexiophiles would like to warmly welcome each and every one of you to this year’s TLL competition. The game is on – the competition has officially started! It’s once again time to find and nominate your favourite blogs/Facebook pages/Twitter accounts and YouTube channels. Last year’s participants will be automatically nominated.

See more and make a nomination >>

“Connections”, a new magazine by, for and about translation professionals

By: Jared Tabor

A new online magazine called Connections has released its first issue. Connections collects interviews, articles and other contributions from translation professionals, and is a product of the members of the Standing Out Mastermind group.

This first issue of Connections contains contributions from, among others, the following members:

Noura Tawil

Pharmacist &Writer,Naturally Translated!

Carolina Garrido

16 years´ experience, MA in education

Anzhelika Kuznetsova

Psychology, Finance and Law

Rea Gutzwiller

Rafa Lombardino

A network of professional translators

You can read the full magazine by clicking on the link or image below:

Read online >>

Terminology introduced for the self-driving vehicle sector

Source: Dealerscope
Story flagged by: Jared Tabor

The Consumer Technology Association (CTA) has announced self-driving vehicle terminology designed to enable a common lexicon among the technology industry and better explain to consumers the terms and concepts of this rapidly innovating sector.

The definitions were developed and approved by CTA’s recently-formed Self-Driving Vehicles Working Group – chaired by Daimler North America and Waymo, and comprised of 34 companies – which also supports driverless vehicle consumer research and policy advocacy.

Among the terms and concepts addressed within the self-driving vehicle terminology:

  • Advanced Driver Assistance Systems (ADAS) or “Driver-Assist” Features: Onboard systems developed to improve safety and performance – examples include lane departure warnings, collision avoidance, adaptive cruise control and automatic braking
  • Aftermarket Technology: Technology services or upgrades provided by companies – unaffiliated with the vehicle manufacturer – added after a vehicle is sold or leased
  • Driving Environment Sensing: The capturing, processing and analysis of sensor data (e.g., cameras, radar, LIDAR) to enhance or replace what a human driver senses
  • MaaS (Mobility as a Service): The shift from personal ownership of transportation modes to shared transportation systems and services
  • Platooning: Synchronous operation of multiple vehicles, often in a convoy, to increase road capacity and efficiency
  • Self-Driving Vehicle: A vehicle capable of fully modeling its environment through an array of sensors, maps and other data in order to navigate and drive without human interaction
  • Urban Mobility: The ability for people in urban and suburban areas to access all modes and forms of transportation.


“The Language of Science and Education: An Expanded Glossary” now available in English and Arabic

Source: University of Arkansas
Story flagged by: Jared Tabor

William McComas’ first doctoral students at the University of Arkansas and at the University of Southern California where he formerly taught were both from Saudi Arabia. So, it’s perhaps no surprise that the idea for one of his recent books was suggested by these Saudi contacts.

“My colleagues in Saudi Arabia wondered if there was a resource that would allow them better access to the literature of science education. They had encountered terms that didn’t translate in the way they were being used in science education. You could look them up in a dictionary but that definition didn’t make sense in the science education context. Essentially, they asked me to write a book to fill this very special need,” said McComas, who holds the Parks Family Professorship in Science Education in the College of Education and Health Professions at the U of A.

This conversation encouraged McComas to produce The Language of Science Education: An Expanded Glossary of Key Terms in Science Teaching and Learning, first published in 2014 by Sense Publishers. That was in English. Now, a new edition has come out from King Saud University Press, which published a new version side-by-side in English and Arabic.

“Every discipline uses words in a context-specific fashion,” McComas said. “For instance, the term ‘informal science learning’ could be confusing because it has a unique meaning in our discipline. Even terms such as ‘laboratory’ and ‘inquiry learning’ could require explanation.”

His Saudi collaborators suggested a list of terms used specifically in science education, and McComas sent these to other science educators for review and to make suggestions for additions. Then, he worked with a team of graduate students and together they researched primary sources to create definitions. Each term in the book has a simple, one- or two-sentence definition followed by a more in-depth discussion of its origin and use in the context of science education.

The original book has been well-received and is cited frequently, McComas said, so he decided to expand on the idea. Now, he and Conra Gist, an assistant professor of curriculum and instruction, are working on one that will be a glossary of the special language of curriculum studies.

See more >>

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