Visualising truth-telling through Indigenous community-specific vernacular photography in Canada and Australia
Bibliographic record
Abstract
The emphasis in Indigenous photographic scholarship has largely been on Indigenous subjects viewed through a colonial lens. It is often assumed that impoverished communities did not have cameras or photographic archives, given the vulnerability and mobility of their lives. However, cameras, although scarce, were present. This is demonstrated in the photographic legacies of Ngarrindjeri families in south-eastern Australia and Qu’Appelle Valley Métis families in Saskatchewan, Canada, investigated in this article. Both groups share similar histories in marginalised settings – ‘one mile camps’ in Australia and ‘Road Allowance’ communities in Canada. The archives created by generations of Indigenous photographers are both familiar and unique. They depict smiling groups posed in front of cars and homes, although the backdrops are very different to the middle-class and suburban settings typical of vernacular photography more widely. Photographic archives in these communities are comparatively sparse, and thus more precious. Importantly, we see the matriarchs who anchored large, extended families, and evidence of their Indigenous knowledges and the survival skills that provided for them. Working with these photographs in deep engagement with communities and their long-held knowledge reanimates these images in contemporary contexts to facilitate the reclaiming of land, connection and family. We argue that such images represent unparalleled forms of truth-telling, offering a nuanced visual history unavailable from other sources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".