Bibliographic record
Abstract
De la marginalisation a la participation de l’auto-determinee : les infrastructures numeriques et l’appropriation de la technologie dans les communautes autochtones isolees du Nord-Ouest de l’Ontario Cet article traite, dans une perspective anthropologique de l’utilisation des infrastructures numeriques et des technologies dans les contextes geographiques et socioculturelles des communautes autochtones du Nord‑Ouest de l’Ontario, Canada. En introduisant le cas de la Keewaytinook Okimakanak Kuh-ke-nah Network (KO-KNET) il analyse d’abord comment les infrastructures numeriques non seulement connectent les gens et les communautes des Premieres Nations, mais permettent egalement des relations entre les communautes locales et les institutions non-autochtones. Deuxiemement, et en utilisant MyKnet.org, le service page d’accueil de KO-KNET, il illustre comment les gens adaptent les technologies numeriques a leurs besoins specifiques dans une region isolee. KO-KNET et ses services facilitent la participation des Premieres Nations aux processus regionaux, nationaux et meme mondiaux sur une base d’auto‑determination et ils permettent aussi des processus de connectivite des TIC au niveau global, contribuant ainsi a la « de-marginalisation numerique » des communautes isolees du Nord-Ouest de l’Ontario.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".