THE CONDITION OF JOJI ILAGAN CAREER CENTER FOUNDATION’S PARTNER COMMUNITY: A HIERARCHICAL REGRESSION ANALYSIS
Bibliographic record
Abstract
This study aimed to establish the significant contribution of individual empowerment to the strength of organizational power to predict Barangay development outcomes. Forty-two residents selected through a systematic sampling technique among nine Purok responded to the survey. The data analysis used frequency count, percentage, mean and standard deviation, hierarchical linear regression, and ANOVA. Results showed an overall moderate level of executive power, individual empowerment, and barangay development outcomes. Moreover, the correlation test showed a solid, positive, and significant relationship between organizational power and barangay development outcomes and personal empowerment and barangay development outcomes. Furthermore, organizational power can predict barangay development outcomes by 62.6%. Finally, the hierarchical regression analysis revealed a significant effect of individual empowerment on the correlation between organizational power and barangay development outcomes. Pointedly, the combined impact of organizational power and individual empowerment can significantly explain the variance in the development outcomes in the barangay. This study’s findings have leadership implications for the barangays. KEYWORDS: intervening effect, individual empowerment, organizational power, barangay, development outcomes, hierarchical regression analysis, Philippines
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".