The Intersections between Sociology and STS: A Big Data Approach
Bibliographic record
Abstract
This paper charts the changing intersections between sociology and science and technology studies (STS) using computational textual analysis. We characterize this "quali-quantitative" approach as a Big Data method, as this calls attention to the commixture of textual and numeric data that characterizes Big Data. The term Big Data, too, calls attention to the increasing privatization of both data and data analytics tools. The data mining was done using a commercial analytics tool, IBM SPSS Modeler, that to the best of our knowledge has not yet been used for STS or sociological research. The identification of intersections occurred as part of a larger project to analyze political-economic and epistemic changes within STS, focusing on academic publishing. These epistemic changes were identified qualitatively, through 76 interviews with STS scholars, and quantitatively, through a computational analysis of three decades of STS journals (1990-2019).
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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".