Mapping the traits desired in followers and leaders onto fundamental dimensions of social evaluation.
Bibliographic record
Abstract
We applied the social evaluation framework to investigate the traits desired in an "ideal" follower, which were compared to the traits desired in an "ideal" leader. Across three studies and five samples, both differences and similarities in role-specific preferences mapped onto the Vertical-Horizontal dimensions of the social evaluation framework in ways that aligned with the demands of each role. Traits higher on the Horizontal-Morality facet (e.g., cooperative, dutiful) and lower on the Vertical-Assertiveness facet (e.g., confident, ambitious) differentiated ideal follower preferences from ideal leader preferences. Focusing on the traits most strongly desired in relation to each role, traits that supported social coordination and collective goal attainment (i.e., work ethic, cooperativeness) were prioritized in relation to ideal followers, whereas intelligence was prioritized for ideal leaders. Trustworthiness was equally valued across both roles. Moreover, we differentiated between necessary and luxury traits by adjusting the budget individuals could allocate toward the desired traits. Investments in necessary versus luxury traits further supported the social evaluation framework and highlighted the need to account for the facet-level distinctions within the Vertical (assertiveness, ability) and Horizontal (morality, friendliness) dimensions. Further, these findings were found to be robust across manipulations (e.g., the target's gender and hierarchical level). (PsycInfo Database Record (c) 2025 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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".