Bibliographic record
Abstract
In this paper, I consider the many ethical dilemmas facing visual anthropologists and those using visual representation material in their research. The issues are many and are complex. With this paper I scratch only the surface of how to confront and deal with some of them. The main purpose of this paper is not to provide solutions, as ethical questions are always unique to the situations in which they develop. What the paper does do is look at ways in which visual anthropologists, and documentary filmmakers have approached and dealt with a variety of these concerns. An extensive review of historical and contemporary works of visual representations are explored and analysed as examples of the types of ethical issues encountered. In our increasingly post-colonial era, issues of voice, co-authorship and copyright highlight just a few of the current topics covered herein. Techniques such as balanced multivocality, reflexivity, collaboration, and reciprocity are discussed with the aid of short case studies to offer examples of the types of ethical issues those using visual material might be faced with and how to possibly deal with (though not necessarily solve) them.
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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".