Lessons in Failing Well: Building Hyper-Migration—a postcolonial, digital, feminist game with refugee youth in Toronto
Bibliographic record
Abstract
“Hyper-Migration” is an experimental collaborative project with refugee youth in Toronto that investigates how storytelling might be employed in a digital platform to meet the needs of this community, addressing issues such as displacement, social marginalisation and a lack of access to educational and job opportunities. This paper reviews our process of elaborating, vetting and instituting a method combining praxis and participatory-action research, informed by feminist, postcolonial, trauma and refugee studies. In an experimental art-based approach that aspires to design failure (Halberstam), the project shifts in strategy and objective as the refugee youth iteratively test and redesign a social action game. This paper explores this process and how critical theory and in-situ game play worked as techniques, driving a focus on local problems and needs, ultimately establishing analogue practices that took on affordances normally ascribed to the digital. As well, the project demonstrates the deep critical abilities of refugee youth to drive critical game design addressing their concerns, and to target key structural, policy and social issues affecting refugee communities that require social change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".