No ‘Museum Piece’: Aboriginal Games and Cultural Contestation in Subarctic Canada
Bibliographic record
Abstract
Abstract Purpose – Using the example of the Dene Games competition, this chapter examines the connections between contemporary sports and the games of the Dene (Athapaskan), a group of indigenous cultures inhabiting the subarctic regions of the Canadian Northwest Territories. Design/methodology/approach – The chapter is based on participant-observation and individual interviews conducted during attendance at the Dene Games gatherings over the course of several years. Findings – I argue that the indigenous Dene Games gathering, where traditional games are organised as a contemporary sports competition, opens a space for the reconstitution of indigenous physical activity practices. The tensions that occur when participation in indigenous games articulates to the practical logic of competitive sports, identify the Dene Games as a space of active cultural contestation. Originality/value – The chapter examines the articulation of historically disparate social practises. It views the hysteretic effects of a pre-existing indigenous physical activity practice as a point of reference for resistance to the normative constraints emanating from the organisational modality of contemporary sports, without offering up an explanation that relies on voluntaristic assumptions of agency. It adopts this perspective in order to avoid grasping the indigenous practice as the operationalised object of its own intervention, a ‘museum piece’.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".