Bibliographic record
Abstract
The very idea of a "canonical data set" implies a whole organization of knowledge: first, the data are durably available—a quarter-century on—thanks among other things to the institutional continuity of the GSS as an important large-scale data-collection enterprise of American social science; second, the data remain meaningful, their validity underwritten by the methods of survey research; third, the disciplinary norms of sociology allow for the possibility of following on someone else's work by reusing the evidence they have already selected; fourth, that evidence can still bear on a significant research question within sociology, a testament to the fruitfulness of the research program in cultural taste and social structure which was set in motion, notably, by the Anglophone reception of Pierre Bourdieu's Distinction. Lizardo and Skiles's starting point, in other words, includes not simply the dataset itself but all the institutional conditions for a productive ongoing research program involving quantitative analysis of cultural data.
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 | 0.202 | 0.470 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.042 | 0.051 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".