Empirical Study of the Structure of Future Police Specialists’ Professional Self-Awareness
Bibliographic record
Abstract
The article discusses the issue of effective training for crime prevention and detection among Ukrainian police officers. Notably, it requires professional knowledge and skills to be developed at a certain level, which means the need for high professional development and a certain level of self-awareness. Thus, training, retraining, and morale of the future specialist are some of the determinatives for the successful completion of the missions that police encounter. Special features of the professional identity of the future police specialist have not been defined and structured yet. The purpose of this study is to highlight the empirical study on professional self-awareness findings, which cover its content and structure, as well as to analyse the authorial model of content and structural components of professional self-awareness among future police professionals that was developed for studies in higher educational institutions. The study analysed the problem of future police officers’ professional identity and devotion to the organisation. The main research methods included the methods of S. Schwartz’s theory of basic human values; methods of R. Cattell’s 16 personality factor model; methods A. Rean and V. Yakunin’s diagnostics of the motives of students’ learning in modification of N. Badmaeva; methods of V. Stephenson’s “Q-sorting” in diagnosing the main trends of behaviour in a real group and ideas about themselves; A. Zverkova and E. Eidman’s test of volitional self-control; R. Schwarzer and M. Jerusalem’s scales of general self-efficacy; methods of N. Hall’s emotional intelligence diagnostics, analysis of the collected data. As a result, during the period of professional training in accordance with the programme methodology established by the authors, future police officers of the control sample developed such personal and social qualities as work capacity, proactivity, respect for social norms and adherence to them.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".