Affecting photos: Photographs as shared, affective ethnographic spaces
Bibliographic record
Abstract
Scholars point to the ubiquity of visual images in media and popular culture as driving striking developments in visual research over the past decade. Yet, with this popularity, there is less attention paid to affective, non-representational dimensions of visual images and specifically to the ways that photos animate and inform ethnographic fieldwork. The felt, sensory qualities photographs hold play a role in not only what gets documented, but also what photos produce as shared, felt objects that circulate during fieldwork. This article redresses a gap in qualitative research literature on the affective, embodied co-experiencing of visual methods that happens during fieldwork by spotlighting a research study on family photographs. In the article, I begin by defining affect, then I profile extant non-representational, affect-driven visual methods and discuss how matter invites affect, and then I spotlight a larger research study I was involved in on visualising the modern Canadian family. In the article, I offer insights that emerged from photo-sharing interviews which produced what I call in the article, affective figured worlds. Built on Holland's concept of 'figured worlds' coupled with Ahmed's notion of 'sticky objects', the article explores the notion of affective figured worlds to attune researchers to more of the non-representational methods in play during visual research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".