The Making of <i>Making Treaty 7</i>: A Practical, Philosophical and Ideological Template for Intercultural Collaboration
Bibliographic record
Abstract
The origins of Making Treaty 7 the theatrical production in some ways mirror the treaty signing process itself 140 years ago: Negotiation, protocol, and the quest of two cultures trying to understand each other. Where this process significantly diverges from the murky dealings in 1877 is that the spirit in which this society and this play were created comes out of a need to repair and reconcile the fractured relationship between the First Nations and “newcomers,” a desire to recognize the facts of the past and have it inform us as to how to move forward together on this land. This article is a unique look inside the genesis and creation of Making Treaty 7. Troy Emery Twigg, a Blackfoot artist, dancer, and educator, and Kris Demeanor, a Calgary-based poet and musician, were part of the initial wave of consultation with Treaty 7 elders, research, and discussion with Michael Green, Narcisse Blood, and Blake Brooker that helped define the trajectory of Making Treaty 7. These interviews illuminate how research, largely based on written versions of first hand oral accounts by First Nations leaders, stimulated the artistic process as “theme bundles” were created and as musical and scene creation collaborations were pursued. Ultimately, it is the story of reconciliation through art, and the blending of socially conscious creation with artistic rigour.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.087 |
| Scholarly communication | 0.035 | 0.023 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".