Bibliographic record
Abstract
The present is a moment of crisis and transition, both generally and specifically in “knowledge” and its institutions. Acknowledging this elicits the key questions: where are we? Where are we headed? What, if anything, can be done about this? And what can the “economics of science” contribute to this? This paper assumes a “cultural political economy of research & innovation” (CPERI) perspective to explore the current upheaval and transition in the system of academic knowledge production, at the confluence of accelerating commercialisation and the seemingly opposing movement of “open science.” This perspective affords a characterisation of the core of the current crises as a crisis of moral economy; an issue to which a political economy of epistemic authority is in turn crucial. A “remoralizing” of knowledge production is thus a matter of key systemic importance, though it is important to understand such developments in power-strategic, and not explicitly moral, terms. Much of the current moves towards “open science” and “massively open online courses” (MOOCs) can also then be seen as self-defeating developments that simply exacerbate the crisis of a viable “economy of science” and in no sense its solution. Their lasting significance, however, is more likely to lie precisely in their effects on the construction of a new moral economy of knowledge production.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".