Bibliographic record
Abstract
Recent years have seen a proliferation of philosophical texts which, without loss of scholarly rigor, are written in prose that is accessible, colloquial, and even humorous. Matthew Strohl’s Why It’s OK to Love Bad Movies represents the apogee of this trend. Strohl’s academic bona-fides are on display during his analyses of topics ranging from artists’ intentions to taste, imagination, and the good life; yet philosophy is the MacGuffin in a study that could more aptly be described as a piece of film criticism, or even a manifesto. The book’s appeal lies ultimately in Strohl’s masterful descriptive writing, which draws the reader through evocative, at times gleeful retellings of scenes from various disreputable movies. These animate the text, which in the end seems less a series of careful arguments than an irresistibly visceral defense of the joys afforded by the medium of film. The central philosophical claims are laid out most clearly in Chapters 1 and 6. In Chapter 1, we encounter the concept of “Bad Movie Love,” defined on page 21 as a sincere rather than sneering appreciation of the ways a film transgresses conventional norms (an account developed primarily through engagement with Susan Sontag’s influential writings on camp). There may be some limited sense in which a film is deemed “bad,” Strohl argues, yet recognition of this badness often occasions a more thoroughgoing enjoyment of the film in question, which indeed may be judged valuable in part because it is bad in this limited sense.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".