Bibliographic record
Abstract
Abstract In this chapter we seize the momentum of the pandemic crisis and its disruption of the film festival world to consider festivals’ stake in the climate and ecological crisis. We will delineate three layers of concern that need to be considered holistically when taking on the challenge of greening film festivals. The first layer tackles the context of festival operations, namely, all arrangements and preparations required to organize festival events. The second layer addresses the emergent discourse of environmentalist media studies, which urges festival scholars to consider critically the consequences of the virtualization of film festivals. The third layer puts the “eco” back into “ecosystem.” The phrase “festival ecosystem” itself is becoming popular in the discourse on film festivals. We think that the time is right to bring what we are calling the festival ecosystem back into a more literal relationship with “environmental media,” media infrastructure and its material relations to the biological environment. This will entail a rethinking of how that ecosystem can be made to work in balance with our planetary needs regarding its natural resources and include acknowledging there are the limits to widespread festival mechanisms that are rooted in logics of growth and abundance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; both teacher heads agree on what is shown here.
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".