Bibliographic record
Abstract
The main thesis of the last chapter has been that we ought to turn to look at what we do when we theorize; that when we do we see that theories serve more than descriptive and explanatory purposes, they also serve to define ourselves; and that such self-definition shapes practice. But if all this is true, I argued, then the use of theory as self-definition also has to be borne in mind when we come to explain, when we practise, social science. For even though theory may be serving us, the social scientists, simply as an instrument of explanation, the agents whose behaviour we are trying to explain will be using (the same or another) theory, or prototheory, to define themselves. So that whether we are trying to validate a theory as self-definition, or establish it as an explanation, we have to be alive to the way that understanding shapes practice, disrupts or facilitates it. But this raises a number of questions about the relation between the scientist's explanatory theory and the self-definitions of his subjects. Suppose they offer very different, even incompatible, views of the world and of the subjects' action? Does the scientist have the last word? Can he set the world-view of his subjects aside as erroneous? But to condemn this world-view does he not have to stand outside it, and is this external stance compatible with understanding their self-definitions? We come here to one of the main issues of the debate around verstehende social science. And this had to arise.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.060 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".