What Makes Mental Associations Personal or Extra‐Personal? Conceptual Issues in the Methodological Debate about Implicit Attitude Measures
Bibliographic record
Abstract
Abstract Over the last decade, a new class of indirect measurement procedures has become increasingly popular in many areas of psychology. However, these implicit measures have also sparked controversies about the nature of the constructs they assess. One controversy has been stimulated by the question of whether some implicit measures (or implicit measures in general) assess extra‐personal rather than personal associations. We argue that, despite empirical and methodological advances stimulated by this debate, researchers have not sufficiently addressed the conceptual question of how to define extra‐personal in contrast to personal associations. Based on a review of possible definitions, we argue that some definitions render the controversy obsolete, whereas others imply fundamentally different empirical and methodological questions. As an alternative to defining personal and extra‐personal associations in an objective sense, we suggest an empirical approach that investigates the meta‐cognitive inferences that make a given association subjectively personal or extra‐personal for the individual.
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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 | 0.097 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".