Bibliographic record
Abstract
On June 12, 2013, a two-and-a-half minute amateur recording titled “Taksim’de Gaz saldırısının içinde kalan Kadının acı çığlıkları” was up-loaded on YouTube. It shows what appears to be a sudden as well as massive tear gas assault being conducted against a large crowd gathered somewhere in the vicinity of Istanbul’s Taksim square, where people have been protesting against the planed demolition of Taksim Gezi Park since May.This video, however, is striking in a number of ways. The speed at which the gas completely fills the whole area where the large crowd is assembled is astonishing. Forty seconds after the impact of the first cartridges, the sky is not visible anymore: a yellowish and dense smoke fills the entire frame of the image. Then, the camera turns its attention to a young woman nearby.1 Like the author of the video, she finds herself caught in the chemical cloud, on top of an immo-bilized bus, apparently unable to flee. The rudimentary respiratory mask she’s wearing over her mouth is clearly unable to protect her adequately in this situation. The incapacitating effects of the gas are dramatically illustrated by the acute distress she quickly finds herself in: the video shows her as she falls on her knees, screaming. The ex-perience must be terrifying. In her precarious position, the only thing she could do to avoid breathing the gas would be not to breathe at all which, in turn, would mean death. As Sloterdijk once observed, her body is coerced into collaborating to its own demise. It has no choice but to interface with the chemical agent filling the atmosphere
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.124 | 0.045 |
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".