Misrepresenting Methodology: A Critique of Epistemological Engineering in Social Science Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it