Attaining the Text: Direct Cinematic Quotation in Video-Essays, Essay Films, and Found-Footage Cinema
Bibliographic record
Abstract
The availability of films on DVD and via online sources, together with the proliferation of inexpensive and easily usable video editing software allow for unprecedented access to the close study and manipulation of film.One result of this has been the recent prominence of the video essay, or audio-visual film criticism.With these new technologies, film scholars, artists and cinephiles are able to rework filmic material, adding imagery or voice-over narration in order to perform critical analysis of the moving image using the medium itself.My project focuses on the video essay as an emerging form of criticism in order to explore and clarify the different paradigms evident in such work.I will also assess the potential value this form of criticism may have for film scholarship.Works of this character have not yet been widely incorporated into academic programs of study, even while many initiatives in the digital humanities are pioneering alternative forms of scholarly productivity.My project will examine important precedents for this kind of work and use existing theoretical writing on film and critical practice in order to elucidate the strategies that these works employ.Works from three contexts will be considered: the current stream of online video-essays, essay films that employ direct cinematic quotation, and found-footage works that use pre-existing films as source material.This thesis will explore how the recent video-essay form can be situated in the broader context of essay filmmaking as well as in relation to text-based film criticism.
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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".