A dialogue and reflection on photohistory: Engaging indigenous communities in research through visual analysis
Bibliographic record
Abstract
Attempts at capturing observations and concerns of change in the Canadian north (sub-Arctic, Arctic) have been mostly conducted through interviews and focus groups spearheaded by researchers. Indeed, images depicting change in the north, when utilized at all, are mostly used to confirm and illustrate the findings derived from researchers. Rarely are local depictions of change used in these interpretations. The purpose of this Notes from the field is to discuss the application of a methodology we term ‘photohistory’ in a study examining visual depictions of cultural and environmental changes in the Moose Cree and MoCreebec First Nations in northern Ontario, Canada. This process of active engagement fosters past reclamation of old photographs while encouraging the discovery of new research directions and partnerships. The application of photohistory in a First Nations located in northern Canada, and subsequent refinement of the methodology for future studies, are discussed.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.040 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".