Bibliographic record
Abstract
We often think of surveillance as ubiquitous, secretive, top-down, corporate, and governmental—and in many ways, it is. Through three vignettes, this essay prods at the ways in which our everyday tools, technologies, and gestures extend surveillance’s reach into our intimate lives and relationships. Each vignette is a story constructed from facts gleaned in news stories, social media, or personal conversations. As such, these vignettes are neither empirical nor entirely speculative. In an effort to consider surveillance as an ongoing and daily activity, they invite readers into more intimate contexts than those that are usually the object of rigorous scholarly analysis. In their intimacy, these stories serve to remind us of the ways in which communication devices are always, in some capacity, tracking and trailing our desires. Vignette 1 tells the story of the NSA agent who uses the agency’s powerful database to spy on an ex-lover. Vignette 2 explores the kinds of information users can get (about themselves) from Big Tech companies, from social media and dating apps. Vignette 3 looks at Internet cookies and their capacity to make unlikely—and unwanted—introductions. Technology, apps, and our always-on devices complicate the boundaries of intimacy and often work to redefine the trajectories of our desire in the process. The breaches of trust detailed in these stories expose the ways in which Big Tech’s desire to predict and to measure human emotion and behaviour exists in tension with our memories, our secrets, and our wild imaginations.
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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".