Bibliographic record
Abstract
Readers of Thomas Pynchon know that well before Foucault's ground-breaking work on the intersections of power and knowledge, surveillance and the mechanisms of social control, these issues had already provided much inspiration for American writers of fiction. Still, interest in all things conspiratorial has perhaps never been higher than now. A number of books in the last few years, aimed at both popular and academic markets, discuss the emergence of this peculiar cultural phenomenon. Oscillating between anxiety and giddiness at one level, fanatical devotion and scoffing disbelief at another, our responses to it suggest that we are nevertheless unable to get our fill of conspiracy theory. A casual search of Amazon.com's books database yields almost 1500 hits for "conspiracy" and "conspiracies," and the profusion of conspiracy sites on the internet is so great that there is no point in trying to estimate how many there are. We may never know Who is behind this flood of conspiracy theories, and we may never know why They want to dizzy us with these ideas–perhaps to distract us from what is Really going on–but there is no doubt that They have been successful in this. After all, if They can get us asking the wrong questions, as Pynchon once pointed out, They don't have to worry about answers.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".