Bibliographic record
Abstract
Abstract Indigenous methodologies in geography have recently been developed to decolonise Western dominated paradigms. It has been argued that research which does not benefit Indigenous communities should not be conducted. However, Indigenous methodologies are not taught in many post‐secondary institutions. Therefore, when they pursue Indigenous topics, many junior researchers are self‐taught in these methodologies. However, these methodologies cannot be defined simply and they are too diverse to be learnt in a short period. In Japan, Indigenous peoples are not widely recognised and research on contemporary Indigenous issues is limited. The concept of Indigenous methodologies is rarely discussed. Because of this, Japanese researchers rarely identify their research as adopting an Indigenous methodology. Indigenous researchers are thereby discouraged from pursuing Indigenous methodologies. Furthermore, a methodology or a thesis statement used by researchers to reflect Indigenous perspectives often gets little support from Indigenous peoples. My master's research on the Ainu mirrored this situation. While Indigenous methodologies remain difficult to learn, junior researchers should not be discouraged from this form of engagement. Practical suggestions are therefore necessary to encourage their use and application. Based on my experience, I suggest that researchers approach Indigenous communities from a learning perspective. This would encourage open‐mindedness and sensitivity. Researchers should also be prepared and willing to refine their research questions and to continue their literature searches after their fieldwork is completed. These strategies could limit misinterpretation and exploitation of Indigenous knowledges and peoples.
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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 | 0.275 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".