Anthropology & Photography
Bibliographic record
Abstract
This bilingual special issue explores the relationship between the anthropological project of understanding humankind and the practice of photography. Adopting a landscape format in full colour to give visual content pride of place, it brings together eleven articles that focus on the photographic process, spanning multiple approaches that overlap considerably: the anthropologist as a photographer; the anthropologist and photographs; photographic anthropology; anthropology photographed; the relationship between anthropologists and photographers. This edition features case studies ranging from Europe and the Mediterranean to the Great North of Canada, the Andes, West Africa, and East Asia. With articles on topics including rural communities, pilgrims, migrants, youth, robots, buildings, administrations, portraits, garbage or clothes, the contributions address the technical and methodological, social, and political, scientific, and deontological issues raised by the use of photography in the context of anthropological research. It proposes contextual and tentative answers to the never-ending question of what a ‘good’ photograph is – or ‘good’ photography –, inspiring consideration of ethics, aesthetics, and emotions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.032 |
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".