Teaching Technology and Livelihood Education in Geographically Isolated and Disadvantaged and Conflict-Affected Areas (GIDCA) Schools in the Philippines: A Narrative Inquiry
Bibliographic record
Abstract
This study explored the lived experiences of teachers as implementers of the agriculture and fisheries curriculum dimension of the TLE content learning area specifically in aligning three essential pedagogic domains: learning outcomes, teaching and learning activities, and assessment tasks within the context of a school categorized as GIDCA of the DepEd as well as the coping mechanisms they employ on specific lived experience. It utilized cooperative inquiry as its design. Data were collected from 12 purposively chosen respondents using a semi-structured interview and analyzed using the Stevick-Colaizzi-Keen framework. Findings showed that TLE teachers could not ascertain whether they are able to align the key learning domains because the content learning area is outside their field of concentration, there is a dearth of available instructional materials intended for the subject matter, and there are little to no opportunities for continuing professional development. To cope with the challenges, they engage in mentoring, access online learning materials, and attend continuing professional development activities. Given this, it is suggested that TLE teachers in GIDCA schools be given.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| 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".