Exploring a Novel Approach to Study Self-Esteem in Children: An Implicit Model
Bibliographic record
Abstract
Self-esteem, an important topic in behavioral research, refers to the positive or negative subjective evaluation of one’s self. Indeed, our feelings of self-worth influence a wide range of domains such as mental well-being, academic success and life satisfaction. Clearly, it is important to foster healthy self-esteem as early as possible in development by assessing the specific factors that promote positive self-esteem in childhood. Yet, this research in children has mostly employed an explicit approach and may not reflect an accurate representation of a child’s self-esteem. Here, I argue for the use of an implicit model in studying self-esteem in providing a more holistic approach. Specifically, I will outline some key weaknesses of a solely explicit model and the benefits of employing an implicit model to the study of children’s self-esteem. Specific methods that can be utilized to measure implicit self-esteem in children will also be discussed. Finally, I will provide possible future directions for the application of a holistic approach in order to investigate specific parental techniques and environmental factors that promote the healthy development of explicit and implicit self-esteem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".