Quarter-Life Crises of Police Officers in Baguio City Police Office
Bibliographic record
Abstract
Not all heroes wear capes. You can see some of them in police uniforms, managing the traffic flows, maintaining peace and order, and protecting the innocent lives of everyone. However, our modern heroes also had to deal with struggles to develop stronger and better characters. This paper deals with the findings on the professional crises experienced by the Baguio City Police Officers. The study’s main objective is to explore the quarter-life crises of Police Officers in Baguio City Police Office and how it affects their work performance. This study has used a qualitative research method through an interpretative phenomenological research design. The key informants of this research were twenty (20) police officers aged 25-30 years old in Baguio City Police Station. The major instrument used for data collection was an interview guide. Using the axial coding technique, the related data revealed codes, categories, and subcategories grounded within the participants’ experiences. The findings revealed that the professional crises experienced by police officers include organizational workload, lack of work-life balance, conflict of ideas, superiority complex, police-citizen encounters, and financial problems. Moreover, the above-mentioned have led them to low morale, low self-esteem, mental strain, weakened police-community ties, career disappointment, poor work performance, and inconsistent sleeping and eating routines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".