Linguistic Diversity Index: A Scientometric Measure to Enhance the Relevance of Small and Minority Group Languages
Bibliographic record
Abstract
Current scientometric indexes do not encourage the linguistic diversity of sources cited in academic texts and researchers are not motivated to cite texts written in smaller languages. This diminishes the cultural diversity of the sources cited and limits the representation of small and indigenous cultures. This text proposes a scientometric measure designed to encourage the linguistic diversity of sources cited in articles, books, and papers. The Linguistic Diversity Index is based on two stipulations: (a) the more linguistically diverse the sources, the higher the score, and (b) the rarer the languages cited, the higher the score. If such a metric were used for the evaluation of social science and humanities journals, it would encourage the publication of papers that cite ideas from rarely represented cultural groups such as indigenous nations, ethnic groups from small countries, and other linguistic groups that have been omitted from mainstream scientific discourse. This might help to produce new research, which would help to improve the situation for these groups and create an epistemology that is more just to small cultural groups.
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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 | 0.030 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.083 | 0.080 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".