Bibliographic record
Abstract
Abstract Indigenous leaders and scholars demand greater respect for their governance and knowledge authority, with one priority the de/centring of the environmental management research-praxis arising out of natural science traditions (Latulippe and Klenk, 2020). That is, to de-centre colonial privilege and centre Indigenous authority. Who can do this and how involves conceptual, political and cultural expertise; yet, natural science disciplinary practices prioritise invisibilizing power, culture and perspective (Latulippe and Klenk, 2020; Vásquez-Fernández and Ahenakew, 2020). This article is an intervention into this context. As a non-Indigenous scholar, I introduce the analytical tools I use to unpack two core assumptions that confounded my ability to hear what Indigenous mentors were saying about environmental management. With two demonstrations—Xaxli’p (Canada) and Gunditjmara (Australia)—I also show how Indigenous leaders do not just present their own approaches, but re-constitute environmental management itself with their meanings, practices, and priorities, whilst environmental management also influences Indigenous knowledge and governance. My focus is with how knowledge is formed and re-formed within and between diverse knowledge holders, including my work as a reflexive modern scholar. Significantly, this article is not purely for edification: this is justice work—in support of both Indigenous people and nature.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.377 | 0.158 |
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".