Feeding the Land: The Importance of Paying Attention to Sakha Language with Traditional Ecological Knowledge
Bibliographic record
Abstract
SUMMARY Through (auto)ethnographic research in the Amga and Megino‐Khangalas uluses (districts) in the Sakha Republic (Yakutia), in this article, we discuss the intrinsic importance of paying close attention to Indigenous languages when exploring Traditional Ecological Knowledge (TEK). Here, language refers not only to vocabulary but also to the kinds of communicative practices or speech acts used to transmit or talk about TEK, especially those that reveal the indivisibility of the physical and spiritual elements in many Indigenous ontologies. Through the presentation of narratives of two researchers—one ethnically Sakha, one not—we highlight the centrality of language to maintaining the integrity of TEK and other Indigenous knowledge. We argue that not only must language be centered and documented to reflect the importance of language choice, but terminology should be situated within stories or narratives to best reveal connections of language to ontology, highlighting the interconnectedness of language and knowledge. [Sakha Republic (Yakutia), autoethnography, Traditional Ecological Knowledge, Sakha language, language usage]
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".