Bibliographic record
Abstract
We present "equiveillance" as a conceptual framework for understanding the balance between surveillance and sousveillance. In addition to this conceptual framework we also present a practical embodiment of equiveillance in the form of a new program called "cyborglogger" ('glogger) that runs on most modern camera phones, along with a server architecture to support 'glogger. Finally we show how the 'glogger program and server architecture create sousveillance communities. Cyborglogger implements features that are ideal for sousveillance such as including continuous capture and real-time upload aimed at communicating personal day-to-day narratives of everyday experiences. The server architecture includes a custom-built community web site that allows a sousveillance community to interact, in real time, with 'glogs from various users of the system. Participants can use their camera phones to display output from other camera phones, resulting in peer-to-peer sharing of visual narratives. This real-time live monitoring creates a social commentary and discourse that runs parallel to the widespread surveillance already present in the world around us. Unlike surveillance, which often happens in secrecy, our tools for sousveillance are freely available and moves personal experience capture into the realm of the everyday, with an open forum for public discourse. We thus explore how users engaging in sousveillance with the 'glogger application provide a balance to existing well-established surveillance practices by examining the philosophical questions that arise from the new artistic practice of sousveillance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".