Measuring many mindsets: A systematic review of growth mindset domains, and discussion of domain relationships, with implications for growth mindset interventions
Bibliographic record
Abstract
Abstract Research into growth mindset, the belief that attributes are malleable, has increased dramatically in the last 30 years, leading to an explosion in the number of mindset domains studied. Given this plethora of mindset domains, there is comparatively little work investigating mindset domain relationships. Further, with expanding interest in growth mindset has come an increase in mindset interventions aimed at increasing growth mindset beliefs, with mixed results. The mindset domain used in intervention messaging is an understudied potential moderator of intervention efficacy, as few domains have been used in interventions, despite the number of domains studied. In this article we raise three questions: (1) How many mindset domains have been studied and what are those domains?; (2) How are beliefs in different mindset domains related to one another?; and (3) How can we use information about existing mindset domains and their relationships to improve mindset intervention efficacy? To address question one, we systematically reviewed the mindset literature between 1995 and 2022 to document studied mindset domains. We then discuss heterogeneity in mindset domain relationship research and suggest how our review can be used to address gaps in this field. Lastly, we describe heterogeneity in mindset intervention efficacy and suggest how to apply our review of mindset domains to examine the impact of the domain used in intervention messaging on efficacy. We aim to stimulate research into understanding mindset domain relationships and how this insight may be applied to mindset interventions to improve people's lives through effectively enhancing their growth mindset beliefs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".