The dominant discourse in Indigenous consultations: when rules impede engagement
Bibliographic record
Abstract
In Canada, consulting with Indigenous communities over recourse projects, the Crown\nsometimes avoids critical engagement with them, holding to the same arguments\nand counterarguments through regulatory and hearing stages. Such hollow moves,\nproduced under the Crown’s rules, become embedded in the dominant argumentative\ndiscourse and pass unnoticed. To detect them, I apply Argument Continuities (AC) – a\nnew category of argumentative discourse analysis. ACs are a set of the same arguments\nand counterarguments repeatedly produced/reproduced by the dominant arguer\nthrough an adversarial reasoning process to dismiss opposing arguments. ACs have\na specific life cycle – a chain of reasoning dynamics developing in a path-dependent\nfashion and increasing the cost of adopting a certain argument/counterargument\nover time. I test ACs in two institutionally diverse cases of Indigenous consultations\nand argue for the contingency of ACs upon the rules of consultations in reasoning\nexchanges. Determining the evidence availability and allocating the burdens of proof\nin consultations, rules make it more or less likely for a dominant arguer to rebut\nopposing arguments with ACs.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.054 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".