Seizing Control? The Experience Capture Experiments of Ringley & Mann
Bibliographic record
Abstract
Will the proliferation of devices that provide the continuous archival and retrieval of personal experiences (CARPE) improve control over, access to and the record of collective knowledge? Or is it possible that their increasing ubiquity might pose fundamental risks to humanity? Through an examination of the webcam experiment of Jenni Ringley and the EyeTap experiments of Steve Mann, this article explores some of the social implications of CARPE. The authors' central claim is that focusing on notions of individual consent and control in assessing the privacy implications of CARPE, while reflective of the individualistic conception of privacy that predominates western thinking, is nevertheless inadequate in terms of recognizing the effect of individual uptake of these kinds of technologies on the level of privacy we are all collectively entitled to expect. The authors urge that future analysis ought to take a broader approach that considers contextual factors affecting user groups and the possible limitations on our collective ability to control the social meanings associated with the subsequent distribution and use of personal images and experiences after they are captured and archived. The authors ultimately recommend an approach that takes into account the collective impact that CARPE technologies will have on privacy and identity formation and highlight aspects of that approach.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".