On the Complexity of Digital Video Cameras in / as Research: Perspectives and agencements
Bibliographic record
Abstract
The goal of this article is to consider the potential for digital video cameras to produce as part of a research agencement. Our reflection will be guided by the current literature on the use of video recordings in research, as well as by the rhizoanalysis of two vignettes. The first of these vignettes is associated with a short video clip shot by a newcomer student as part of a three-year research project that focused on the interrelationships between citizenship, technology, and pop culture. The second vignette relates to the entry of a piece of art into the research agencement. As an agencement in and of itself, the goal of this article is not to provide definitive responses, but rather to disrupt habitual ways of thinking about videos in / as research and potentially contribute to change.
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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 | 0.130 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.021 | 0.122 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".