Rhetoric of psychological measurement theory and practice
Bibliographic record
Abstract
Metascience scholars have long been concerned with tracking the use of rhetorical language in scientific discourse, oftentimes to analyze the legitimacy and validity of scientific claim-making. Psychology, however, has only recently become the explicit target of such metascientific scholarship, much of which has been in response to the recent crises surrounding replicability of quantitative research findings and questionable research practices. The focus of this paper is on the rhetoric of psychological measurement and validity scholarship, in both the theoretical and methodological and empirical literatures. We examine various discourse practices in published psychological measurement and validity literature, including: (a) clear instances of rhetoric (i.e., persuasion or performance); (b) common or rote expressions and tropes (e.g., perfunctory claims or declarations); (c) metaphors and other "literary" styles; and (d) ambiguous, confusing, or unjustifiable claims. The methodological approach we use is informed by a combination of conceptual analysis and exploratory grounded theory, the latter of which we used to identify relevant themes within the published psychological discourse. Examples of both constructive and useful or misleading and potentially harmful discourse practices will be given. Our objectives are both to contribute to the critical methodological literature on psychological measurement and connect metascience in psychology to broader interdisciplinary examinations of science discourse.
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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 | 0.170 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.009 | 0.099 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".