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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".