Self-Concept and Time Perspective of Convicts
Bibliographic record
Abstract
Objective: The purpose of the article is to connect the time perspective of the convicts with their self-concept. Background: After an extended stay in places of deprivation of liberty, it is difficult for convicts to adapt to the conditions of life in freedom, to restore the social ties that were lost and weakened due to isolation. And therefore, they often fall into the marginal part of the population, cannot find their place in life, and often replenish the number of unemployed or, even worse, re-enter the criminal path and commit crimes. Method: The following psychological methods were used: the technique of “Who am I?”, which allowed studying the cognitive component of the self-concept; the methodology of personal differential that provides an opportunity to study the emotional component of the self-concept; the self-attitude test questionnaire and the method of F. Zimbardo allowed to determine the main features of the time perspective. Results: The article reflects the study of convicts based on which a typology of “self-concept” of convicts was proposed. Besides, the results of a study of the temporal perspective of convicts depending on the type of self-concept are presented. Conclusion: The authors are inclined to believe that a detailed study of this problem will identify and provide effective ways to stimulate the constructive resocialization of various categories of prisoners.
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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 | 0.002 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".