Picturing social inclusion: photography and identity in Downtown Eastside Vancouver
Bibliographic record
Abstract
This thesis offers an exploration of the relationship between photography and identity in the marginalised urban space. I focus specifically on the annual Hope in Shadows photography contest in Downtown Eastside Vancouver (DTES) and through field-based focus groups, engage with individuals in the DTES community to develop a deeper understanding of what resident-led photography means to them. I position urban photography as revealing “the entanglements of the individual and the city” (Lancione, 2011) and demonstrate how photographing and viewing photographs of the neighbourhood allows participants to articulate important links between space, place, self and community. Drawing on existing literature in visual sociology, my study explores the potential of resident-led photography in emancipating participant lifeworlds from their excluded status, opening up multiple avenues to social action. I argue for the potential of the camera in person-centred research: promoting a recognition of C.Wright Mills’ (1959) “personal troubles” as “public issues”, encouraging dialogical understandings between urban in-groups and out-groups, and enabling the (re)assertion of affirmative social presence for excluded communities. Focussing on the Hope in Shadows contest as case-study, I explore how community photography can create opportunities for identity representation, (re)creation and recognition, and promote social inclusion in the city.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".