Algorithmic Realities and the Canadian Aporetic Condition: Digital Counterpublics and Epistemological Justice
Bibliographic record
Abstract
Interactive projects from the National Film Board of Canada demonstrate how algorithm-driven storytelling can illuminate the structural tensions that define the Canadian aporetic condition. Through a close study of Bear 71, The Space We Hold, Biidaaban: First Light, and Do Not Track this paper demonstrates how code-based interfaces encourage participants to co-produce knowledge that challenges settler governance, data capitalism, and extractive ecological logics. The analysis blends media studies, public-sphere theory, and the aporetic framework to trace connections among wildlife surveillance, urban futurism, testimonial memory, and personalized data dashboards. Each project cultivates digital counterpublics in which Indigenous sovereignty, ecological interdependence, survivor authority, and data-justice activism gain discursive traction. The findings suggest that immersive design can promote epistemological justice – fair access to knowledge production and recognition of diverse ways of knowing – by redistributing representational power, visualizing previously hidden infrastructures, and expanding civic imagination within a publicly funded platform. These insights suggest practical pathways for cultural institutions seeking to align interactive media with democratic resilience and equitable futures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".