“Depth of field” or “negative forms”: space/syntax from HDR digital photography to hypermedia navigation
Bibliographic record
Abstract
L’article explore les notions touchant la perception du contraste champ / figure dans les arts. Partant de l’oire des mots “négatif” et “profondeur de champ” en photographie au XIXe siècle, il étudie des solutions comme les combinaisons du sujet et de l’arrière-plan à partir de photos sous-exposées et d’autres surexposées ; il place la question dans un contexte chronologique plus large d’interaction entre le premier plan et l’horizon, avec procédés de cadrage et de facture dans les paysages classiques, le rôle des ombres en peinture depuis Leonard de Vinci jusqu’aux théories des Lumières sur la perception, l’exploration des effets atmosphériques et de l’éclairage par les artistes et les savants victoriens allant des décors de théâtre aux expériences physiques, et il conclut sur les versions actuelles avec les effets de lumière ou de flou dans les programmes de modélisation 3D ou les échanges entre le premier plan et l’arrière-plan dans les médias interactifs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".