Festivals of films, decolonial spaces, and public pedagogy : some preliminary reflections
Bibliographic record
Abstract
The present paper offers reflections on festivals of films as potential spaces for public pedagogy, decolonization, and emancipation. The aim is to examine some dilemmas inherent to spectatorship, which overlap with pedagogical dilemmas around the establishing of communities and the recognizing of students — the oppressed, the individual, the collective — as persons. Drawing on the works of Brazilian educationalist Paulo Freire, French philosopher Jacques Rancière, Carol Roy, Elizabeth Ellsworth, and others, I offer ways in which film and its deployment within festivals might open up generative spaces of imagination for students, educators, community members, and festival programmers. In order to do this, I argue that watching films and attending festivals should be considered along their collective and individual correlates; that the human dimension of festival participation differentiates it from isolated spectatorship; and that festival programmers can help to place viewers in positions of potential emancipation if they take care to organize films around dialogue, understanding that a key educational dimension of festivals is that they call us into relation with others. In attempting to “demythologize” film and festivals of films in this manner, I make a preliminary attempt to redeem or reconceptualize the festival as a decolonized space of politics, culture, and solidarity building.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".