Misrepresenting Methodology: A Critique of Epistemological Engineering in Social Science Research
Bibliographic record
Abstract
Among the most pervasive issues currently debated in the social sciences pertains to scientific misconduct. The discourse on scientific misconduct has burgeoned in the last three decades and has come to permeate multiple arenas, including academia, industry, and public policy. While interest in this area has imparted critical insights into understanding and regulating the phenomenon, some commentators have argued that it is time to expand the scope of what acts precisely qualify as scientific misconduct—beyond its conventional definition that conflates the term with fabrication, falsification, and plagiarism. In responding to this line of critique, this article focuses on a neglected aspect of scientific misconduct, though one which is particularly prevalent in social science research—namely, the case of researchers offering disingenuous claims related to a study's methodology. To explicate how this form of misconduct in science materializes into action, this article revisits Bruno Latour's careful tracing of scientists in laboratories. Through his analysis, Latour captures the disjuncture in the rhetoric and the practice of methodology in empirical research. Integrating Latour's critique with the concept of agential realism, we present one philosophically grounded avenue by which to resolve this form of scientific misconduct in future social science research.
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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 | 0.306 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.024 | 0.286 |
| Scholarly communication | 0.034 | 0.039 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.029 | 0.040 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".