Against capture: notes on recording and the problem of extraction in education research
Bibliographic record
Abstract
Against Capture offers a methodological intervention to the current popularization of videomaking in participatory visual research, a genre of arts-based method, with a particular emphasis on the regime of extraction that is ignored in one of its most significant steps: recording. Often rendered as a visual metaphorical substitute or a backdrop, the author discusses how colonial relations to the built and natural environments have set the conditions for extractive practices where audio-visual recording as a form of capture demands a reimagined relational ethics to place. Within this extractive relation, the author observes how built and natural environments are understood as readily available and unlimited aesthetic resources for artmaking. This intervention seeks not to be prescriptive but rather offers a descriptive account of the epistemological and ontological contradictions in the current celebration of innovative approaches to research in education.
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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 | 0.178 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.035 | 0.178 |
| Scholarly communication | 0.036 | 0.046 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.018 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".