A Long Night: An Animated Documentary as a Tool to Represent Difficult Knowledge in Public Spaces: \nTransforming Compassion into Action
Bibliographic record
Abstract
A Long Night: An Animated Documentary as a Tool to Represent Difficult Knowledge \nin Public Spaces: Transforming Compassion into Action \nA Long Night is a 12-minute animated documentary that draws on oral narrative and uses metaphor and symbolism to explore difficult stories of illness, hunger, and torture endured by three Syrian former political prisoners who survived captivity and are now residents of Montreal. For this work of research-creation, I recruited the three narrators through my connections in the Syrian Canadian community and conducted oral history interviews to collect testimonies in the context of the Syrian political history. While the content can be challenging, A Long Night emphasizes the courage of the speakers and the importance of taking action, where possible, against injustice. \nUsing the framework of difficult knowledge and Foucault’s power and knowledge duality, the film references historical trauma and interprets difficult narrative while exploring its benefits in shaping a new historical consciousness that has been silenced for decades. The film A Long Night is a research-creation project that focuses on adopting strategies to guide its audience to transform their compassion into action while simultaneously being a cultural product that is easy to disseminate through social media and other accessible platforms. \nOverall, feedback collected from the audience suggests that the medium of animated documentary is effective at communicating difficult narratives, such as systematic torture and mass violence. Furthermore, it can do so without alienating the audience, and may prompt positive action.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".