Making Space: Reading the Truth and Reconciliation Commission of Canada's Report in and Beyond the Classroom through Practice-Based Research
Bibliographic record
Abstract
In a graduate-level Digital Storytelling course in the Department of Communication at the Université de Montréal, the first project I assign is called a “Collective Experimental Story.” The intention of this project is to introduce students to collaborative storytelling and to explore a platform that enables participatory forms of presentation and co-creation. I enter into this experimental process with students. In Fall 2021, I proposed that the project respond to the Truth and Reconciliation Reading Challenge. From 2008 to 2015, Canada’s Truth and Reconciliation Commission produced a report documenting the history and ongoing impacts of the country’s residential school system on First Nations. This report includes 94 Calls to Action, including a call for teachers at all levels to address these histories and their effects in the classroom. Students in my course were excited by this proposal. Over the first seven weeks of the course, we read the report, defined the objective and approach of our project, conducted research and development to identify a suitable platform, and divided tasks. We used Gather Town—an online meeting platform that boasts an old-school pixelated video game interface—to stage a live event. The goal was to share what we had learned and to open space for dialogue. Participants circulated as avatars in our simulated spaces. In this article, four of us who were involved in the project describe our practice-based research process.
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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.037 |
| Scholarly communication | 0.028 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".