Linguistic Diversity Index: A Scientometric Measure to Enhance the Relevance of Small and Minority Group Languages
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it