1 COLONIALISM ISN’T THE ONLY OBSTACLE: INDIGENOUS PEOPLES & MULTILEVEL GOVERNANCE IN
Bibliographic record
Abstract
It should be impossible to think of multilevel governance simply in terms of federalism or to limit the discussion to those settler governments that comprise the federation (national, provincial/state and local). Yet, most do despite the fact that doing so ignores the existence of Indigenous nations and their structures of governance. This standard also ignores the legacies of colonialism and constitutional imperatives that recognize Indigenous peoples as having a different relationship with the state than (other) citizens and other governments. Thus, discussions pertaining to multilevel governance in settler societies such as Canada, United States and Australia (and arguably even in New Zealand which despite being a unitary system has both colonial and Iwe or tribal governments) need to overcome this intellectual hurdle and begin the process of decolonizing our understanding of multilevel governance. While there is a tremendous need to bring Indigenous into the discussion of multilevel governance and to put Indigenous women at the forefront of that discussion, there is an even greater need to recognize and understand difference for Indigenous peoples are neither part of settler society nor the settler state. Indigenous governments, Indigenous political movements/organizations (gendered and otherwise) and Indigenous women’s attempts to
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".