Governing Ourselves: Reflections on Reinvigorating Democracy Stimulated by Gitxsan Governance
Bibliographic record
Abstract
This chapter explores how we might, by our practice, give more vigor to the democratic aspiration that a people should rule themselves. It does so in two steps. First, it examines the form of governance of the Gitxsan people, a First Nation of northern British Columbia. The traditional governance of the Gitxsan, like that of most Indigenous peoples, is not organized in the manner of a state. The nature of Gitxsan members’ attachment to their legal and political order is not masked, then, by the heavy institutionalization of a state, and the characteristics of their adherence can be perceived and weighed more easily. Second, the chapter reflects upon how a similar quality of adherence might be achieved within state-structured polities. In short, this chapter uses the Gitxsan comparison to seek more precision in how we ought to understand citizens’ attachment to – their “consent” to – their legal and political order, and it suggests practical steps that might promote that end in contemporary states.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".