Methodological issues in the validation of implicit measures: Comment on De Houwer, Teige-Mocigemba, Spruyt, and Moors (2009).
Bibliographic record
Abstract
J. De Houwer, S. Teige-Mocigemba, A. Spruyt, and A. Moors's normative analysis of implicit measures provides an excellent clarification of several conceptual ambiguities surrounding the validation and use of implicit measures. The current comment discusses an important, yet unacknowledged, implication of J. De Houwer et al.'s analysis, namely, that investigations addressing the proposed implicitness criterion (i.e., does the relevant psychological attribute influence measurement outcomes in an automatic fashion?) will be susceptible to fundamental misinterpretations if they are conducted independently of the proposed what criterion (i.e., is the measurement outcome causally produced by the psychological attribute the measurement procedure was designed to assess?). As a solution, it is proposed that experimental validation studies should be combined with a correlational approach in order to determine whether a given manipulation influenced measurement scores via variations in the relevant psychological attribute or via secondary sources of systematic variance. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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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 | 0.045 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.062 | 0.078 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".