Cambridge Social Ontology: Clarification, Development and Deployment
Bibliographic record
Abstract
Social ontology—the study of the nature and basic structure of the social realm—is currently enjoying a period of sustained growth and development, both as a field of study in its own right and as a project concerned with under-labouring for a variety of different social scientific disciplines including economics. One of the most active streams in this area emanates from Cambridge and a group of researchers operating at the interface between social ontology and heterodox economics whose work is sometimes identified as Cambridge Social Ontology. The central figure in this project is Tony Lawson, whose work has provided much of the impetus for Cambridge Social Ontology over the last thirty years. This Special Issue of the Cambridge Journal of Economics is intended to mark the contribution Lawson has made to the study of social ontology and to the application of its results to economics and the social sciences more widely. It does so by presenting a range of new papers whose authors were invited to engage with the work of Lawson and his colleagues in the Cambridge Social Ontology project. The intention was to encourage new work, whether it be critical or constructive in orientation, and thereby hopefully to advance the themes that Lawson has pursued over the course of his career.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".