Bibliographic record
Abstract
For the past several months I have been conducting an experiment at airport security gates, shooting photographs of the Transportation Security Administration (TSA) facilities and screeners to determine how long I can go on before I will be asked to stop. After shooting photos in 12 airports I have received only one warning at the US-Canada border while taking a picture of a twenty-something woman of colour being interrogated by TSA workers after she was physically searched in a nearby makeshift room. I only became visible to the TSA at the moment I witnessed her visibility, but in general as a white woman I go relatively unnoticed in a US security regime largely based on racial profiling. If I were a person of colour it is possible that many of these images would not exist, that my camera would have been taken, the images destroyed, or I might not have even taken the risk in the first place. In any case, it has become clear to me that the airport is no longer just a ‘non-place’ as Marc Auge (Auge, 1995) famously described it over a decade ago, but in the context of the US-led war on global terror it has possibly become ‘the place’, a charged and volatile domain punctuated by shifting regimes of biopower.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".