Visual Research and Social Justice – Guest Editors' Introduction
Bibliographic record
Abstract
Traditionally, the sources of data in qualitative methodologies have been overwhelmingly linguistic, relying on the written and spoken word (Crang, 2010;Holliday, 2000;Pauwels, 2010).Ethnographic research in particular has seldom incorporated visual methods of data collection and analysis, although anthropologists and geographers have long used photographs, maps, sketches, and paintings to portray "Native Informants" (Spivak, 1999), "the field," and the practice of fieldwork (Crang & Cook, 2007;Edwards, 1992).Margaret Mead (2003), for example, used visual "salvage anthropology" as a mechanism for "preserving" vanishing cultures by producing a permanent photographic record of them that scholars could study once those cultures disappeared entirely.Perhaps this hesitance to engage with visuals in qualitative methodological repertoires is related to the crisis of representation, specifically to epistemological critiques of visual knowledge that foreground the role the "visual gaze" played in colonial appropriation and other projects of domination (Gilman, 1986;Gregory, 2003;Lewis, 1996;Said, 1993).It may also be linked to overdrawn claims for the representational facticity and verisimilitude of visual materials, for the "obviousness of vision that allows one to imply a transparency about the world and picture, that can suggest the visual offers 'raw data' as if bypassing troublesome issues of constructing knowledge" (Crang, 2010, p. 212).The imperialist histories of visual knowledge and disputes about its epistemological status have, until recently, clouded our sense of how visual methods might be put to critical use in qualitative research as a way to understand and make sense of various aspects of social life.Because visual
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".