Meaningful Engagement: Women, Diverse Identities and Indigenous Water and Wastewater Responsibilities
Bibliographic record
Abstract
Les auteurs de cet article sont un collectif compose de membres des Premieres Nations, des metis, ils habitent la ville ou sur les reserves, ils sont jeunes, il y a des meres, des grands-meres, ils sont les gardiens du savoir autochtone, des historiens, des etudiants ingenieurs et d’autres en sciences sociales Nous avons travaille ensemble sur “Naanaagide’enmodaa Nibi: Occupons-nous de l’eau,” un projet fonde par le Reseau des eaux canadiennes. Cette initiative veut promouvoir l’autonomie des communautes autochtones et leurs decisions se rapportant a l’eau et aux eaux usees qui historiquement etaient soumises a des controles dictes par le gouvernement pour la qualite de l’eau, la securite. Souvent les projets etaient en conflit avec le savoir traditionnel et local de l’eau. Dans un esprit de collaboration, le projet cherche a etablir des facons d’inciter les dirigeants autochtones, les ingenieurs des Premieres nations et ceux de l’Ouest, les autochtones instruits ainsi que les membres des communautes Inuit et Premieres Nations a travailler ensemble, creant ainsi un cadre specifique aux communautes et culturellement approprie. En particulier, nous regardons comment le savoir traditionnel autochtone et la connaissance scientifique pourraient se recouper par le biais de nos relations et des nouvelles responsabilites face a l’eau.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".