Digital flames: public storytelling, climate, and reconciliation at National Film Board of Canada (NFB)
Bibliographic record
Abstract
"Public storytelling carries social responsibility during ecological breakdown and war. This article examines three National Film Board of Canada (NFB) projects – Biidaaban: First Light (Lisa Jackson), Bear 71 (Leanne Allison with Jeremy Mendes), and Losing Blue (Leanne Allison) – and shows how they cultivate attention, relation, and action. The analysis builds on the Canadian aporetic condition (see Bessai) and Albert Murray’s stylization of experience, which shape perception into shareable form and civic practice. Across web, VR, and cinema, the projects present complexity with restraint and organize learning through voice, tempo, mapping, and sequence. Biidaaban models language resurgence in a VR Tkaronto foregrounding Anishinaabemowin place-names; Bear 71 renders an interactive corridor ecology and turns surveillance traces into civic reflection; Losing Blue witnesses the life of ice through steady images and measured narration linked to field knowledge. The article outlines design principles for public-service storytelling in a climate emergency – proximity, reciprocity, slowness, invitational structure, continuity, and accountability – and specifies institutional routes for use in classrooms, libraries, festivals, and the open web. NFB infrastructure supports commissioning, curation, and circulation that keep works available for study and practice. These projects equip audiences with shared language and habits of attention that aid collective response to climate and conflict."
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".