Bibliographic record
Abstract
Adolescents encounter challenges in their development primarily encompassing psychological, ideological, and moral aspects. As time evolves, conventional methods to address these issues have been gradually weakened in practice, necessitating the introduction of new approaches. This paper provides an overview of the role of guilt in the growth of adolescents. Based on existing research results, this study introduces the definition, generation mechanism, moral attributes, and driving force of guilt through methods including citation introduction, summarization, analysis, and inference. Subsequently, the positive and negative effects of guilt are summarized. On the one hand, guilt brings negative emotions such as self-loss and anxiety, and may lead to an anti-social personality. On the other hand, it enables a more stable mental state in adolescents, promotes beneficial behaviors, and improves ideological and moral levels. The analysis results show that guilt, with the assistance of “conscience”, urges adolescents to take responsibility for their actions, correctly deal with the adverse consequences of their behavior, and make positive changes, thereby providing impetus for the development and improvement of adolescent personality. Consequently, it is recommended to introduce educational strategies centered on guilt into adolescent educationandguarantee their rationaizedapplication.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".