Bibliographic record
Abstract
Kate Crawford has recently suggested that the everyday lived experience of big data is one of “surveillant anxiety”: the fear that the personal information that individuals disclose about themselves and that others generate about them is intercepted and analyzed by the intelligence services within emergent praxes of pervasive dataveillance. I empirically assess this notion of “surveillant anxiety” in the context of spatial big data. Drawing on the results of a small-scale survey of understandings of location data collection and dissemination via mobile devices and their contextualization against other available data, I demonstrate that individuals are seemingly more concerned with transparency in data collection and in controlling flows of personal spatial information about themselves than they are with practices of data capture or their eventual use(s). Rather than a generalizable societal condition of “surveillant anxiety,” I argue that the realities of living in a (spatial) big data present are better characterized in terms of what I designate as “anxieties of control”: the desire to discern (be aware of) and direct (determine the disclosure of) personal spatial big data flows about oneself while feeling that any attempt at exerting such control is effectively futile.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".