Evaluating the Potential of Cybercartography in Facilitating Indigenous Self-Determination: A Case Study with the Hupačasath First Nation
Bibliographic record
Abstract
Cette étude évalue une approche particulière de la cartographie numérique, la cybercartographie, en tant qu’outil d’autodétermination autochtone. L’étude, réalisée auprès des Premières Nations du Canada, s’appuie sur les principes autochtones de propriété, de contrôle, d’accès et de possession pour reconnaitre les moyens précis par lesquels la cybercartographie peut aborder certains aspects de l’autodétermination. Les résultats montrent que les exigences en matière d’applications cybercartographiques sont propres à chaque communauté, et que ces applications peuvent faciliter l’autodétermination quand les communautés participent activement à la sélection de la technologie pendant le processus de recherche. L’étude de cas présentée ici révèle que la cybercartographie, et la cartographie numérique en général, peuvent véhiculer d’importants éléments culturels autochtones et servir à rehausser les épisodes éducatifs pendant lesquels se transmettent les connaissances entre les générations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".