Bibliographic record
Abstract
Through this paper, I will consider the roles that the humanities can play in interpreting and interacting with the arts. To investigate this topic, I will use several international examples. These are situated in Cannes, Edinburgh, and London, though they directly connect with countries such as South Africa and Mozambique. The world's best known film festival (Cannes), and the world's largest theatre and arts festival (Edinburgh), alongside the world's first national public museum (the British Museum in London), provide the contexts in which my argument develops. In each of these spaces, one can be confronted by a myriad of human faces, presented publically in innumerable ways. Film posters, stand-up comedy adverts, and exhibition fliers commonly employ the human face to attract, to intrigue, and to entice audiences toward their spectacle. The humanities can both interact with and critically analyse these uses of faces. The human faces in these diverse and dynamic settings provoke questions which the public humanities can address, as they interrogate celebrity, analyse portrayals of suffering, and in the shadows of dangerous memories, even help to create materials to inspire peace.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.058 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".