Bibliographic record
Abstract
The attacks on New York and Washington on September 11, 2001, lit up the global landscape. Not only in these two cities, but wherever the news and the pictures reached during the first hours after the planes struck-all over the planet, therefore-there were people quickly able to make out features of the contemporary world that they had not previously taken in, or taken the measure of fully, things that challenged their earlier expectations and existing frameworks of understanding. Not, however, in one quarter. With a section of the Western left, the response was as if everything remained just as it had always been. Leave aside the callousness in much of the left's response toward the human dimension of the tragedy; but in explaining the crime of 9/11 the same thin categories that had been deployed in one conflict after another during a decade and more were instantly pressed into service. Imperialism and blowback-that was pretty much all one needed to understand what had befallen the citizens of Manhattan, the passengers on the planes, and the workers at the Pentagon, and there were accordingly people content to describe the attack as a comeuppance. The crime that so brutally illuminated the contours of the international political landscape thus revealed at the same time a frozen structure of concepts and assumptions. With the aid of it, many on the left shielded themselves from realities they didn't want to see or to assign their proper weight. In what follows I comment on some aspects of this theoretical nexus.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".