Co-Learning with Land-Based World Building in Yvette Nolan’s <i>The Unplugging</i>
Bibliographic record
Abstract
In this conversation, settler professor of Indigenous literature and drama, Brenda Vellino, and Mi’kmaq social work student Carolyn Simon, at Carleton University, collaboratively explore learning with both the script of Yvette Nolan’s The Unplugging and the Great Canadian Theatre Company’s 2023 production. Given the world-building proposals in Nolan’s play in the aftermath of a near-future “unplugging,” the authors situate their discussion in a continuum with other interventions in the genres of Indigenous speculative storywork or what Anishnaabe scholar Grace Dillon terms “Indigenous Futurisms.” The conversation is shaped around four sub-themes enacted or implied in the playscript and the 2023 Great Canadian Theatre Company production: intergenerational learning and knowledge keeping, staging queer Indigenous intimacy through non-binary casting, world-building possibilities in a time of apocalypse, and generative land-based teachings and dramaturgy. As a pedagogical intervention, this conversation is also prefigurative of the possibilities of Indigenous–settler exchange based on mutual respect. It is offered in the spirit of the work Nolan’s play does around the potential repair of Indigenous and settler relations enacted between the characters of Elena, Bern, and Seamus.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.022 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".