A Response to Michael Mayerfeld Bell's “In the River: A Socio-Historical Account of Dialogue and Diaspora”
Bibliographic record
Abstract
I respond to Michael Bell's account of how the people of Grenadier Island fended off the threat of dislocation by the St. Lawrence Islands National Park project and instead established a working partnership with Parks Canada. This was contrasted with the far-from- desirable outcome of the Between the Rivers people in our attempts to gain recognition of our cultural heritage that was transformed forever by forced dislocation for the Land Between the Lakes National Recreation Area. I attempt to compare and contrast the context and efforts of the Grenadier and the Between the Rivers people and examine our current situation in light of the hope that the Grenadier case exemplifies. I conclude with a suggestion for further research and analysis.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".