A Moment of Reckoning, an Activation of Refusal, a Project of Re-Worlding
Bibliographic record
Abstract
I came to the theatre to discover my own humanity—to discover a belief in my own humanity. I looked to the theatre to teach me the worth of my own life on this earth and to teach me to fearlessly articulate this humanity, to manifest it that I might find the bridge between ‘us’ and ‘them,’ between my ancestors and their oppressors, between myself and those who oppressed me, and between my yet-unborn descendants and the peoples among whom they might someday live. I came to the theatre to save my life and to try, through this medium, to transfigure a salvaged wreck into a life well lived. I am still seeking that bridge between… Across these territories, a great reckoning is taking place. The fuse is burning, and, it seems, an explosion is imminent—an explosion of the narrative through which this nation that calls itself ‘Canada’ has spoken itself into being. In this plague year, talk of ‘truth and reconciliation’ between treaty peoples has evolved into a speech act that activates survivance. Indigenous artists, activists, and scholars, with increasing vigour and conviction, are choosing to activate their refusal of that long-cherished narrative—its content and structure—and the institutions that nourish and uphold it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".