School, Family, and Community Dynamics: Challenges and Opportunities from Pandemic to Post-Pandemic
Bibliographic record
Abstract
Two consecutive years have passed since educational institutions globally were closed due to the Covid-19 pandemic. In the first quarter of 2020, classes at all levels in the Philippines were suspended, and alternative means of completing the respective school year have been very challenging. Implementing Modular Distance Learning (MDL) was the best option for the Department of Education to continue learning. This phenomenological research described the school, family, and community dynamics, the challenges experienced, and the opportunities encountered in transitioning to face-to-face classes in Bislig City Division. The researcher used an interview guide to gather the responses from individual interviews and focus group discussions. Word cloud applications for the categorization of themes were also used. Findings showed that the COVID-19 pandemic has snatched the students' significant mental, emotional, and physical health; challenges of infection, adjustment to the new normal, and learning gaps brought about by the distance learning modality were related. However, assurance of students’ holistic preparedness and support to school were the major themes that emerged. Thus, the role of family, school, and community were interconnected and deemed essential in promoting better learning outcomes. Opportunities were also evident amidst the educational crisis. Its advantages were strong collaboration among stakeholders, promotion of economic activities, and a safe learning environment.
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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 | 0.005 | 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 | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".