“But they were never only the master’s tools”: the use of photography in de-colonial praxis
Bibliographic record
Abstract
The majority of anthropological literature around photography of Indigenous peoples has privileged the actions, agency, and intent of the Western photographer. While one cannot ignore the significant colonizing influences that photographs have had on Indigenous peoples, one cannot presume that these individuals were solely the silenced subject or victim in a one-sided, inferior relationship with the camera and its operator. Recent scholarship is now re-examining the relationship Indigenous peoples have had with photography as a culturally productive technology since its development. In this article, I will address the role of photography in a global context as an object and a method of decolonization in two ways: (a) through archived collections of colonial photography and (b) in the production of contemporary photographs by peoples who experience contemporary colonialism. In both of these contexts, I will explore how various populations or communities with a colonial history use photography to confront ongoing legacies of colonialism, particularly in agendas aimed at repairing and reconfiguring relationships with self, family and kin, colonizer, community, and the natural world. Drawing on examples from several countries and locales, I will address the potentials and challenges of reframing Indigenous peoples’ experience of, and relationship with, photography within the context of de-colonial praxis.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".