Retention and Job Satisfaction Among Pilipino and Offshore Teachers: A Comparative Study
Bibliographic record
Abstract
This study aimed to compare the retention and job satisfaction among Pilipino and offshore teachers. The descriptive correlational design was used in the study with 40 offshore teachers and 43 Pilipino teachers as respondents of the Cagayan State University. The data gathered were analyzed using mean descriptive statistics, standard deviation, correlation techniques, and inferential statistics such as the t-test. The result of the study reveals that the offshore teacher respondents are mostly employed in the USA, in public schools, at the elementary level, married, and have an average age of 52.23 and an average teaching experience of 21.05 years. The Pilipino teachers almost have the same proportion in the high school and elementary level, 51% are married, and 44% are single, almost the same proportion of male and female, have an average age of 38 and average teaching experience of 12.53 years. The job satisfaction level of offshore teachers is significant and satisfying for Pilipino teachers. Offshore teachers are significantly more satisfied than Pilipino teachers. The retention level for offshore teachers is 2.55 years and 1.35 years for Pilipinos. Pilipino teachers have significantly higher retention levels than offshore teachers, as shown by the lower number of schools served. Overall, the job satisfaction level of offshore and Pilipino teachers is not significantly associated with any of the profiles studied. Offshore teachers’ retention level is significantly associated with their location (US or Canada). Pilipino teachers’ retention level is significantly associated with their number of years of teaching experience. For both offshore and Pilipino teachers, there is no significant association between retention level and job satisfaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".