Bibliographic record
Abstract
Preface Unstable Self-Esteem: Using the Temporal Stability of Self-Esteem to Better Understand Feelings of Self-Worth Variations in Multi-Dimensional Physical Self-Concept Linked to Physical Education & Physical Activity Clubs in Underserved Urban High School Students Religion & Self-Esteem: A Study among 13- to 15-Year-Old Students in the UK Self-Esteem as a Predictor of Academic Success in Health Professions Degree Students The Early Transition to Secondary School in Germany: Effects on the Level & Relations of Self-Esteem The Role of Siblings for Childrens Self-Esteem Discrimination, Relationships & Ethnic Identity as Predictors of Self-Esteem & Social Competence among Early Adolescent Immigrants in Canada Reversion Effects of Self-Esteem on Responsibility Orientations among Young People Sources of Self-Esteem of Chinese Migrant Mothers Living in Hong Kong Can High Self-Esteem Protect Children from Drug Abuse? Relations of Self-Concept Clarity with Impulsivity & Hubris among Chinese University Students Self Esteem & Information & Communication Technologies Intergroup Discrimination: The Positive Negative-Asymmetry Effect (PNAE) & Private Collective Self-Esteem Who Uses In-group Identification & Favoritism for Buffering Threats to Self-Esteem, & When Do They Use It? Index.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.022 |
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".