Bibliographic record
Abstract
The “Philosophical Foundations of Economic Science” have afforded occasion of sporadic inquiry for most of this century. 1 Since the appearance in 1953 of Friedman’s famous essay on “The Methodology of Positive Economics” 2 scholarly interest has increased greatly 3 and has advanced considerably in sophistication by the attempt to apply Lakatos’s generalization of the Kuhn-Popper-Feyerabend debate 4 to the methodological problems of our discipline. 5 It is now become commonplace, within the “mainstream” tradition at any rate, to appraise putative contributions to economic knowledge in terms of “progressive” or “degenerating” “Scientific Research Programmes” 6 and, presumably, to accept the epistemological consequences of that view. Knowledge of the economy, like knowledge of other parts of the physical universe, is socially created within a community of experts bound together by common allegiance to a set of criteria for distinguishing truth from falsehood. Though these criteria are historically conditioned, and may well be ideologically tainted, commitment to the — generalized — falsification principle keeps the fresh air of critical scrutiny circulating in the community, so preserving it from permanent lapse into dogmatism and error. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| 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 | 0.024 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".