Methodological issues in the validation of implicit measures: Comment on De Houwer, Teige-Mocigemba, Spruyt, and Moors (2009).
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
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).
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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