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Record W1983069088 · doi:10.1007/s13280-014-0531-x

Multilinguismo nas ciências ambientais: Ahora ya! (Multilingualism in Environmental Sciences: It’s About Time!)

2014· letter· en· W1983069088 on OpenAlexaboutno aff
Meredith Root‐Bernstein, Richard J. Ladle

Bibliographic record

VenueAMBIO · 2014
Typeletter
Languageen
FieldComputer Science
TopicWeb visibility and informetrics
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEnglish languageMultilingualismLimitingFirst languageLanguage barrierQuality (philosophy)LinguisticsPsychologySociologyPolitical sciencePedagogyMathematics educationEngineeringLawEpistemology

Abstract

fetched live from OpenAlex

Comment to: Bortolus, A. 2012. Running like Alice and losing good ideas: On the quasi-compulsive use of English by non-native English speaking scientists. AMBIO 41: 769–772. doi:10.1007/s13280-012-0339-5. Writing in Ambio, Bortolus (2012) recently argued that many non-native English-speaking environmental scientists invest a disproportionate amount of time in over-coming language barriers, limiting the impact of their research, and constraining their productivity. While it may be inevitable and possibly desirable that one language dominates academic communication, most native English-speaking academics never need to adapt to a work environment dominated by another language, and may not appreciate the magnitude of this problem. We are both English language native speakers (US and UK) who have lived and worked for several years in South America (Chile and Brazil). Like many “expat scientists,” our experiences have given us new perspectives on the importance of language in science. Specifically, we have come to believe that universities in all countries (English and non-English speaking) are missing out on remarkable opportunities by failing to take advantage of the latent scientific capacity in non-English-speaking countries. Indeed, some of the policy challenges are surprisingly similar in English-speaking (ES) and non-English-speaking (NES) countries, with both needing trained personnel who can bridge the language gap. For scientists in NES countries, the most pressing need is usually to convert their research into high quality English language manuscripts that will be judged purely on the basis of scientific merit. Currently, many of these scientists pay large amounts for private translation services—many of which provide poor value for money because they do not clearly understand the science, the structure of scientific papers, or the terminology. Alternatively, bilingual colleagues provide correction and translation services at discount rates or, more typically, as a favor; the first option is a waste of scarce funding resources, while the second is often a waste of bilingual researchers’ time. One obvious solution would be for departments (or universities) in NES countries to hire professional translators with a background in the subject. Alternatively, and more radically, departments could offer attractive positions for bilingual or native English-speaking researchers, where a percentage of their time is earmarked for capacity raising with colleagues. Such positions could be permanent or implemented through offering positions to visiting academics. More radically, journals could take on some of the burden of translation by providing them as a free part of the publishing service. The impacts of such a strategy in terms of attracting new readership and increasing submissions could be considerable. For example, Ambio translated all issues into Chinese (Mandarin) as part of its publishing service for more than 15 years (1993–2008), reporting many benefits to the journal (Kessler 2009) including a marked increase in articles submitted by Chinese researchers (Soderstrom, personal communication). Translators also have a potentially important role in universities in ES countries. Scientific production is growing throughout the developing world, notably in Brazil and China. Translating research papers into any of the main languages spoken internationally (e.g., Mandarin, Spanish, Portuguese, French, Russian) could significantly boost a scientist’s citation rate. The capacity for foreign-language citation generating and publishing is enormous. Data from Google Metrics indicate that the top ten current h5 indices for journals publishing in Mandarin, Portuguese, Spanish, and French languages are an order of magnitude smaller than the top journals in English. At the same time, the total productivity in environmental, biological, and agricultural sciences for countries speaking those four languages currently accounts for a fifth of global publishing (Table 1). Table 1 Potential for publication in four major non-English languages, 1996–2012. Documents published in the categories of earth sciences, environmental sciences, agriculture, evolutionary biology, and molecular biology in countries among the top 50 most ... One way to productively take advantage of this gap is to fill it with multiple translations into target languages (English to non-English and vice versa). This will raise the h indices and impact factors of non-English publications—a situation which benefits both journals and researchers under current productivity metric regimes. Arguably, it is also good for science due to the greater sharing of ideas and the more rapid accumulation of support/refutation of hypotheses. Such a strategy would also increase the knowledge sharing of publications originally in English, especially in applied areas (such as agronomy) where English language publishing, citing, and language competency may be low. There are already multiple models for publishing translations. Some of these can be implemented already, although many researchers are likely to be unaware of them. Some English language journals currently publish abstracts in Spanish or French (e.g., Conservation Biology, Journal of Mammalogy and Canadian Journal of Zoology). Other journals, such as the Natureza e Conservacao (The Brazilian Journal of Nature Conservation), publish papers in multiple languages. There seems to be little reason why in the future online-only versions of full translations could not be provided by the same journals that publish the original papers. This especially makes sense under the rapidly expanding pay-to-publish open access model. In addition, there are some journals that sometimes publish translations of papers originally published in other journals, e.g., Iberoamerican Communication Review. A more straightforward option is to post a translation of an article on a personal website or under “unpublished materials” on scientific social networks such as ResearchGate (www.researchgate.net). Another forward-looking approach to disseminating material in multiple languages would be for major universities to develop online, freely available archives of their research publications translated into targeted foreign languages. This could be an exciting way for universities to increase their profiles in other countries, and has the power to generate new citations and collaborations by breaking down existing academic networks. Thus, rather than relying on personal interactions with bilingual researchers to exchange ideas, universities could extend their international networks by the simple expedient of making their researchers’ papers available in the native languages of major scientific nations. Scientific communication in a variety of media is increasingly valued. Communication in other languages should be considered an aspect of this trend and should be incentivized and rewarded. Even if scientific publishing moves toward the decontextualized database model advocated by some commentators (e.g., Priem 2013), science will remain a social activity. The language in which we communicate affects the confidence with which we express ourselves, our ability to convey complex and appropriately nuanced ideas, our snap judgments of how intelligent other people are, and even our personalities. Ipso facto, we need good translators for good science.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.272
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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