Bibliographic record
Abstract
This chapter shows how academia maintained relative autonomy in the face of party interests during the so-called Thaw. A young academic outsider, Ernst Strnad (1928), tried to mobilize, without success, his party relations against the best known senior economists in order to receive a doctoral degree. The question at stake was the status of political economy as an exact science, in particular regarding the role of statistical methods. Considering the significant changes economics experienced in Western countries after the rise of the econometric movement in the 1930s, what would a socialist critique of “bourgeois” statistics look like and what would be the role of quantitative methods in political economy? Strnad’s thesis tackled these questions, but it was written unsolicited without supervision. Facing difficulties in finding academic approval, he tried to mobilize high-party officials in his favor. In spite of the increased party influence, universities were not willing to give up their academic ethos and maintained the traditional mechanism of academic inclusion and exclusion.
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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 | 0.041 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".