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Record W2972352818 · doi:10.1177/0741088319861648

Addressing the “Bias Gap”: A Research-Driven Argument for Critical Support of Plurilingual Scientists’ Research Writing

2019· article· en· W2972352818 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWritten Communication · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsYork University
Fundersnot available
KeywordsCritical reflectionSociologySecond language writingArgument (complex analysis)Scientific writingPsychologyPedagogyPublic relationsPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

This article outlines findings from a case study investigating attitudes toward English as the dominant language of scientific research writing. Survey and interview data were collected from 55 Latin American health and life scientists and 7 North American scientific journal editors connected to an intensive scholarly writing for publication course. Study findings point to competing perceptions (scientists vs. editors) of fairness in the adjudication of Latin American scientists’ research at international scientific journals. Adopting a critical, plurilingual lens, I argue that these findings demand a space for more equity-driven pedagogies, policies, and reflective practices aimed at supporting the robust participation of plurilingual scientists who use English as an additional language (EAL). In particular, if equity is indeed a shared goal, there is a clear need for commitment to ongoing critical self-reflection on the part of scientific journal gatekeepers and research writing support specialists.

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.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.505
GPT teacher head0.507
Teacher spread0.002 · 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