Bibliographic record
Abstract
Taking photographs to capture moments is not a novel activity. Taking pictures is an activity we are so accustomed to that it often escapes conscious thought; it has become habitual and ingrained in the human experience. The digital age of photography brings a sense of comfort in photography that has made documenting memories easier, but it also brings a new form of surveillance and data harvesting. Digital photo cataloguing applications like Google Photos and Apple Photos quantify every image they store and capture metadata and location data. These applications process and assign the images to ‘auto-generated’ albums without user authorization. This study aims to understand what data is being captured and to what extent by digital photo cataloguing applications. Withdrawing from digital photographic mediums, this study uses analog film mediums to capture daily life. Throughout this experience, I conducted an auto-ethnographic study on photography practices. Findings introduced and proved the primary concern of data surveillance in digital photo cataloguing applications. While the process of resistance proved to be inefficient on some ends, it provided great insight into how analog media is a strong medium of resistance against digital data colonialism.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".