The viability and validity of the Authentic and Hubristic Pride scales: Reply to Dickens and Murphy (2023).
Bibliographic record
Abstract
Dickens and Murphy (see record 2023-63008-001) claim that the Authentic and Hubristic Pride (i.e., AP/HP) scales (see record 2007-02840-009), which we developed and validated over 15 years ago, do not validly assess the theoretical constructs of authentic and hubristic pride (e.g., Tracy & Robins, 2004a, 2007). These authors further call for the development of new measures based on a top-down approach, which would incorporate the theory into scale items. Although we appreciate Dickens and Murphy's emphasis on the need for valid assessment tools in this important research domain, we disagree with their conclusion that the extant scales are "fundamentally invalid." Here, we explain why a top-down approach would not be preferable to the bottom-up one we used and review the relatively large body of evidence supporting the validity of the extant AP/HP scales. Dickens and Murphy also raised several concerns regarding the HP scale specifically; most of these, as we explain, are either incorrect, exaggerated, or valid concerns but not ones that invalidate the HP scale. Nonetheless, we agree with Dickens and Murphy's suggestion that the AP/HP scales could be improved, and we echo their call for future research in this vein. Finally, we recommend that scholars seeking to advance the field in this way adopt the "living document" approach advocated by Gerasimova (2022). (PsycInfo Database Record (c) 2023 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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.036 | 0.084 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".