Bibliographic record
Abstract
With a mantra that ‘nobody is as smart as everybody,’ Google maintains giant databases that enable unprecedented quantitative analysis of a social world that is now majority urban. A Wall Street web-based broker offers an App that promises to turn ‘thoughts into trades.’ Economists and marketers now use evolutionary theory in ‘neuroimaging’ brain scans of consumer behavior. The United States Air Force now trains more joystick-maestro drone pilots than old-fashioned Do-the-Right-Stuff human fighters and bombers. In this article, I suggest that these trends signal a troubling reincarnation of an aggressive Right-wing (post)positivist zombie reanimated by global, digitized neoliberalism. The long-forgotten revolutionary project launched by Auguste Comte in 1822 was hijacked and corrupted after his death, and now we confront a hybrid cyborg zombie, a dehumanized, automated adaptation to the post-structuralist situated knowledges of consumer sovereignty. #Comtebot: The positivist era is right now; the question is what kinds of politics it will have.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.041 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".