A historical and ethical analysis of the constitutive effects of cameras
Bibliographic record
Abstract
In this paper it is argued that cameras have constitutive effects on subjects and society and that these constitutive effects can have an undesirable impact on autonomy and emancipatory progress. By means of a historical analysis it is shown that cameras have shaped and directed social norms, people’s behavior, people’s perception of the world, and people’s self-formation. This historical analysis also teaches that cameras’ constitutive effects are often intended. In other words, cameras are often actively used as a tool to exercise constitutive power, which means that their impact on the world and the subject is not predetermined, but contingent. These insights from the past are especially valuable considering the fact that advancements in computer vision technology now make it possible to employ cameras for new purposes, such as augmented reality, automated surveillance, emotion recognition, facial recognition and machine vision. Learning from the history of the camera helps to take a critical stance towards these emerging smart cameras applications and ensure that, with their power to change the individual subject and society at large, smart cameras support autonomy and emancipation.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".