Bridging Parallel Rows: Epistemic Difference and Relational Accountability in Cross-Cultural Research
Bibliographic record
Abstract
To what extent are non-Indigenous researchers invited to engage the knowledges of Indigenous peoples? For those working within a western paradigm, what is an ethical approach to traditional knowledge (TK) research? While these questions are not openly addressed in the burgeoning literature on TK, scholarship on Indigenous research methodologies provides guidance. Reflexive self-study - what Margaret Kovach calls researcher preparation - subtends an ethical approach. It makes relational, contextual, and mutually beneficial research possible. In my work on contested fisheries knowledge and decision-making systems in Ontario, Canada, a treaty perspective orients my mixed methodological approach. It reflects my relationships to Indigenous lands, peoples, and histories, and enables an ethical space of engagement through which relational accountability and respect for epistemic difference can be realized.
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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 | 0.125 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.023 | 0.154 |
| Scholarly communication | 0.030 | 0.047 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".