Bibliographic record
Abstract
Abstract Traditionally, geographic research and engagement with Indigenous communities have largely been developed within a western research paradigm and have historically been linked to colonial practices such as extraction and/or domination. The consequences of these research practices and paradigms have been the further marginalisation of Indigenous people globally. However, geographers are increasingly being influenced by a range of Indigenous scholars from both within and beyond the discipline who highlight the colonial foundations of geographic knowledge and the ongoing production of colonial relations, and who are calling for a decolonisation of knowledge through the use of Indigenist methodologies. After exploring this shift, this paper moves to a discussion of my engagement with research in Indigenous communities using Indigenist methodologies, including the emotions and thought processes that emerged during my own research journey, which led me to southern Guyana and the Makushi and Wapishana peoples who reside in the northern savannah environments of the Amazon basin. I conclude by sharing how I am continuing that journey using Indigenist approaches in my work in the Madre de Dios region of Peru, and by encouraging future scholars to challenge traditional geographic research methods.
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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 | 0.057 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.046 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".