Bibliographic record
Abstract
Last year at the 2153 conference of the Canadian Anthropology Society, a renowned linguist, Leahcim Suark, urged us to document written languages before they disappear. In his now famous speech, titled, “World Literacy in Danger,” Suark brings alarming statistics on the condition of written languages of the world. According to Suark, one written language is lost approximately every two years. By next century, Suark claims, nearly half of the roughly 70 remaining written languages on Earth will likely disappear. The loss of literary languages brings about significant challenges in preserving human knowledge, accessing information, and maintaining linguistic diversity. Yet, in this commentary, I argue that oral traditions present a more productive way to think about knowledge transmission and preservation. Drawing on ethnographic data in Ajyy Sire, the traditional territory of the Ajyy Djono, I show that in a society where oral communication prevails and knowledge is transmitted through oral traditions across generations, information becomes more accessible, irrespective of a person’s literacy or computer proficiency. I also show that without the dominance of written (standardized) languages, oral languages and their diverse expressions can still flourish, fostering resilience amidst the global changes facing humanity.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".