Bibliographic record
Abstract
In June 2015, Canadians received the Calls to Action, 94 recommendations of the Truth and Reconciliation Commission (TRC 2015c). Indigenous Studies scholar Paulette Regan (2010) calls for “socio-political actors in Canadian society” (p. 23) to commit to an unsettling pedagogy that creates “decolonizing space for Indigenous history—counter-narratives of diplomacy, law, and peacemaking practices—as told by Indigenous peoples themselves” (p. 6). My settler initiative takes Regan’s call literally, asking: What happens when actors read the 94 Calls to Action aloud in community? Enaction of the Calls remains underachieved; 13 (arguably 8) have been completed in 6 years. Following Métis artist/scholar David Garneau’s (2016) proposal of “irreconcilable spaces of aboriginality” (p. 26) this settler intervention reconceptualizes a familiar acting practice; we brought an Indigenous-led policy document to the rehearsal floor, relying on the actor’s embodied practice to dialogue with the story of the text. Rereading the Calls in the Zoom/studio, we lifted the text from the page to our listening body, speaking our discomfort, complicity, and hope. This exercise is prelude: an oral action to provoke unsettling conversation, to seed commitment to action, to prepare the playground for collaborative encounter with Indigenous colleagues (Call 83). Enaction is our responsibility. My research analysis takes two forms: a script (Chapter 4) and an academic inquiry (Chapter 5); both query reading aloud as oral resource for unsettling. We found evidence of settler inaction that underlies the story (and necessity) of the Calls. We named our complicity. As storying citizens, I argue theatre makers take responsibility for the politicising impact of our storying craft. Moments of mirth surprised us. We found evidence of decolonizing possibility in a pedagogy of play. I ask: is there a generative role for the actor’s practice of complicité (as playful collusion) and transgressive play in restorying relations between Indigenous and non-Indigenous peoples? We are called to reconsider; I name sites of reconsideration. In conclusion, I urge we continue reading the charge (including treaties) aloud in community to ignite and deepen our collective commitment to enaction of the Calls, as preparatory step toward respectful reciprocal (re)conciliation action in Canada.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.026 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".