THE RELATIONSHIP BETWEEN ALEXITHYMIA AND AGGRESSIVE BEHAVIOR AMONG ADOLESCENTS
Bibliographic record
Abstract
This study scientific aims at examining the problematic of the relationship between Alexithymia and aggressive behavior and to reveal Differences in level of alexithymia and aggressive behavior among adolescents according to the gender variable (males and females),In this field study participated a group of Composed 120 adolescents in Mohammédia (Morocco), their age range between 15 and 18 years old.The study used two scales: Toronto alexithymia scale (TAS-20) and the Buss Perry Aggression Questionnaire (BPAQ).The results of this study confirmed a statistically significant correlation Positive between alexithymia and aggressive behavior, as confirmed by the value of the correlation "Person" (r =0.367, p<0.01).Our study also confirmed the possibility of predicting aggressive behavior through the level of alexithymia .likewisewith statistically significant differences in the level of alexithymia according to the gender variable (male and female), with no differences a statistically significant recorded in the level of aggressive behavior according to the gender variable as confirmed by the value of the "T-test" .These results are consistent with the conclusions of studies that examined the relationship between the alexithymia variable and behavior aggressive in adolescents.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".