Understanding Recording Technologies in Everyday Life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electronic recording and surveillance systems are arguably some of the most pervasive technologies in the world today. Despite this rapid proliferation and their study by many researchers, there is still work to be done in understanding how people reason about these technologies when they encounter them. In this article, the authors describe attitudes, perceptions, and concerns regarding electronic recording encountered in daily activities. They present data gathered from interviews grounded in real experiences that form the basis of a discussion for how people develop mental models about the intent and uses of a broad scope of recording technologies embedded in the world. Individual constructions of reality about current recording systems, including the people, places, and activities that surround them, provide insight into how design, technology, and policy can work together to provide appropriate information about the existence and uses of recording devices. These insights can lead to usable systems that allow individual users to make informed personal decisions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it