Shaping film festivals in a changing world: practice and methods
Bibliographic record
Abstract
This volume is a collective attempt on the part of a community of academics, film festival curators, and archivists to come to terms with practical and intellectual challenges of the pandemic and post-pandemic realities affecting cultures of film festivals. The collection draws contours of critical inquiry orienting current film festival research and practice to explore new directions in archiving and decolonizing practices and big data analysis in the post-Covid-19 context and beyond. The four-part study gathers the voices of academics and practitioners who engage in a dialogue to articulate critical areas for both study and practice of film festivals, and identifies conceptual tools to address them: “Archival Turn,” “Decolonizing Film Festival Studies,” “Post-Covid-19 and Film Festival Studies” and “Data Visualization and Film Festival Research and Practice.”
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 | 0.071 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".