Do pride and shame track the evaluative psychology of audiences? Preregistered replications of Sznycer <i>et al</i> . (2016, 2017)
Bibliographic record
Abstract
Are pride and shame adaptations for promoting the benefits of being valued and limiting the costs of being devalued, respectively? Recent findings indicate that the intensities of anticipatory pride and shame regarding various potential acts and traits track the degree to which fellow community members value or disvalue those acts and traits. Thus, it is possible that pride and shame are engineered to activate in proportion to others' valuations. Here, we report the results of two preregistered replications of the original pride and shame reports (Sznycer et al. 2016 Proc. Natl Acad. Sci. USA 113 , 2625–2630. ( doi:10.1073/pnas.1514699113 ); Sznycer et al . 2017 Proc. Natl Acad. Sci. USA 114 , 1874–1879. ( doi:10.1073/pnas.1614389114 )). We required the data to meet three criteria, including frequentist and Bayesian replication measures. Both replications met the three criteria. This new evidence invites a shifting of prior assumptions about pride and shame: these emotions are engineered to gain the benefits of being valued and avoid the costs of being devalued.
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.
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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".