A Meta-Analysis of Measures of Self-Esteem for Young Children: A Framework for Future Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to synthesize information from literature on measures of the self in young children to create an empirical framework for developing future methods for measuring this construct. For this meta-analysis, all available preschool and early elementary school self-esteem studies were reviewed. Reliability was used as the criterion variable and the predictor variables represented different aspects of methodology that are used in testing an instrument: study characteristics, method characteristics, subject characteristics, measure characteristics, and measure design characteristics. Using information from two analyses, the results indicate that the reliability of self-esteem measures for young children can be predicted by the setting of the study, number of items in the scale, the age of the children being studied, the method of data collection (questionnaires or pictures), and the socioeconomic status of the children. Age and number of items were found to be critical features in the development of reliable measures for young children. Future studies need to focus on the issues of age and developmental limitations on the complicated problem of how young children actually think about the self and what methods and techniques can aid in gathering this information more accurately.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it