Effects of matching personal and organizational mindsets on belonging and organizational interest.
Bibliographic record
Abstract
Growth mindsets are beliefs that abilities, like intelligence, are mutable. Although most prior work has focused on people's personal mindset beliefs, a burgeoning literature has identified that organizations also vary in the extent to which they communicate and endorse growth mindsets. Organizational growth mindsets have powerful effects on belonging and interest in joining organizations, suggesting that they may be a productive way to intervene to improve individual and societal outcomes. Yet, little is known about for whom organizational mindset interventions might be more or less effective, a critical question for effective implementation and theory. We examine whether people's personal mindset beliefs might determine the effect of organizational growth mindsets, and if so, whether this moderation reflects a matching or mismatching pattern. Three experiments manipulated the espoused mindset of an organization and found that organizational growth mindsets primarily increased belonging and interest in joining among participants who personally endorsed matching growth mindset beliefs. An additional field study provided ecological validity to these findings, replicating them with students' experiences of belonging in classrooms. This study also revealed a divergent mismatching pattern on grades: rather than bolstering the grades of students with growth mindsets, growth mindset classroom contexts primarily enhanced the grades of students with more fixed mindsets. By clarifying for whom organizational growth mindsets are beneficial and in what manner, the current work provides theoretical and practical insight into the psychological dynamics of organizational growth mindsets. (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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".