Bibliographic record
Abstract
In this chapter, I explore approaches to research that explicitly consider the relationships between Western and Indigenous methodologies. In so doing, I engage with various interpretations of Homi Bhabha’s (1994) Third Space, métissage, and other articulations of hybridity as discussed by a variety of scholars from distinct cultural and epistemological perspectives in relation to Indigenous methodologies. I reflexively revisit some of my own past work in this context as a Métis scholar in Canada of mixed Indigenous and European ancestry as well as that of several leading and emerging Indigenous scholars. This line of inquiry concludes with presentation of inspiring examples and discussion of the implications for research in Indigenous environmental studies with a specific focus on the interconnected areas of natural resource management, land-based tourism, outdoor leadership, and education. Through this discussion, I aim to contribute to the relatively limited ( Neilsen & Wilson, 2012 ), but growing, collection of Indigenous voices in tourism studies and related fields.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".