Embracing collaborations between festivals and higher education: A case study of the ‘Decolonising film festivals and curating African cinemas’ networking event at King’s College London
Bibliographic record
Abstract
In recent years, there has been an increasing awareness of the social responsibility of Higher Education, encouraging knowledge exchange initiatives and impact. This often involves the collaboration with the industry, embracing a curatorial turn in the pedagogic approach. This self-reflexive case study shares the learning, challenges, and opportunities offered by the organisation of a networking event named ‘Decolonising Film Festivals and Curating African Cinemas.’ In so doing, it seeks to offer insights into one such forms of collaboration between Higher Education and the Industry. Through an analysis of the feedback by participants and the discussions at a round-table on decolonising, it highlights the horizontalism and distended environment of the experience, fostering a safe and fruitful discussion that engages in a call to action towards to sought change. In public facing events hosted at the university, the classroom becomes a brainstorming exercise in collaboration. The curatorial turn adopted through collaboration bridges theory and practice. Itlurifies voices in the learning and teaching experience, and collaboratively rehearses potential creative solutions to real life scenarios. It promotes social justice, engaging all participants in the process.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".