Photography and philosophy: essays on the pencil of nature
Bibliographic record
Abstract
Acknowledgments. List of Figures. Contributors. Introduction (Scott Walden, New York University). 1. Transparent Pictures: On the Nature of Photographic Realism (Kendall L. Walton, University of Michigan). 2. Photographs and Icons (Cynthia Freeland, University of Houston). 3. Photographs as Evidence (Aaron Meskin, University of Leeds and Jonathan Cohen, University of California, San Diego). 4. Truth in Photography (Scott Walden, New York University). 5. Documentary Authority and the Art of Photography (Barbara Savedoff, City University of New York). 6. Photography and Representation (Roger Scruton, University of Buckingham). 7. How Photographs Signify: Cartier-Bresson's Reply to Scruton (David Davies, McGill University). 8. Scales of Space and Time in Photography: Perception Points Two Ways (Patrick Maynard, University of Western Ontario). 9. True Appreciation (Dominic Lopes, University of British Columbia). 10. Landscape and Still Life: Static Representations of Static Scenes (Kendall Walton, University of Michigan). 11. The Problem with Movie Stars (Noel Carroll, Temple University). 12. Pictures of King Arthur: Photography and the Power of Narrative (Gregory Currie, University of Nottingham). 13. The Naked Truth (Arthur C. Danto, Columbia University). Epilogue. Bibliography. Index.
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 | 0.002 | 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 | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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; 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".