Bibliographic record
Abstract
The globalization of communication in ‘major’ languages has become incompatible with the claims made by the other languages. Many minor, ‘lesser used’ languages were formerly marginalized and ignored because of their incompatibility with national policies; more recently, while acknowledged by specialists, they still have had to struggle to be more publicly recognized as vehicles for important literature, and also in some cases as actually existing. Having the Nobel Prize for Literature awarded is not necessarily effective: within years of Frédéric Mistral’s Nobel prize few people would have acknowledged the existence of Provençal as a language. One potentially more profitable means of achieving recognition is through being translated into better-known languages. The paper will look at two examples.
 
 
 
 First: Slovene, the language of just 2 million people in Europe; a language with an established literature; officially a national language; but not generally known. Promotion through translation has been extraordinarily active: great efforts have been made to translate all the major works of literature into ‘major’ languages. Among the results: an enormous translation factory, where sometimes quality is sacrificed to quantity; and very high pay for translators.
 
 
 
 Second, at the other end of the ‘status-as-a-language’ spectrum: Lakhian, which very few people recognize as a ‘language’ rather than a dialect; and yet one that received huge (if temporary) recognition when the one person who wrote what is recognized as ‘serious’ literature in Lakhian, Ondra Lysohorsky, had his poetry translated by Boris Pasternak and W.H. Auden.
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 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".