Terminological Impoverishment: The Effects of Contemporary Scholarly Publishing Practices on Terminology and Knowledge Dissemination
Bibliographic record
Abstract
Historically, translation played a key role in knowledge dissemination, but since the mid 20 th century, scholarly publishing has moved steadily towards the use of a central language (English). Colonial practices, the incentivization of publishing in English, and the lag (or lack) of term development in other languages has led to the terminological impoverishment of many of these languages across multiple research domains. Consequences include the continued hegemony of English language and western culture and epistemologies, limited uptake of research findings in non-Anglophone communities, and a digital gap between English and other languages with regard to the development of linguistic resources and tools. Emerging approaches for addressing terminological impoverishment include terminology planning, retro-terminologization, broader consultation with communities (including Indigenous communities), and developing resources and tools for low-resource languages. In parallel, it is critical to recognize that reforms to research assessment are needed to value and encourage knowledge dissemination in languages beyond English, and that terminologists must raise the visibility of their profession and skills in order to be included in more efforts aimed at rectifying terminological impoverishment in scholarly research fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.262 | 0.515 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.021 | 0.062 |
| Scholarly communication | 0.044 | 0.040 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".