Revisiting Mianscum's ‘telling what you know’ in Indigenous Qualitative Research
Bibliographic record
Abstract
In his 1972 court appearance, (James Bay Cree vs. James Bay Energy Corporation), François Mianscum was asked to swear on the Bible to “tell the truth”. The Cree hunter had been summoned to account for his “way of life” and speak about the impact of massive development on his traditional hunting ground by the construction of hydroelectric dams. After contemplation and deliberation of a seemingly routine court request his translator responded, “He does not know whether he can tell the truth. He can only tell what he knows.” (Richardson, 1975, P. 46) Cited widely through Clifford's (1986) inclusion of “the story” for its importance, how can his words guide researchers' approach to “honesty” in qualitative inquiry? This paper turns to the significant members of Mianscum's life to ask how they interpret his statement and what messages can be gained from it while carrying out research in both the indigenous and non-indigenous context.
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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 | 0.305 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.046 | 0.122 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".