The Futures of Leadership: Assessing the State of Governance in Talalora District amid the COVID-19 Pandemic
Bibliographic record
Abstract
The world was never the same again when COVID-19 came. For managers, business owners, and employees alike, it was like something they have not seen before. For school leaders, it was a challenging and overwhelming task to run the school amid the lockdowns and community quarantines. This study aimed to assess the state of governance in Talalora District, one of the 34 districts in the Division of Samar, through a descriptive-quantitative research design, the state of school operations, fiscal management, implementation of DepEd programs, projects, and activities, and human resource development. This study reveals the aspects such as late submission of reports, difficulty in communicating with teachers and school personnel because of intermittent internet connection, and a decrease in productivity were noted for teachers as they were overworked in accomplishing their tasks. The MOOE allocation per quarter is not enough to cover all school expenses as there are additional purchases such as COVID-19 kits, vitamins, and other measures to help stop the spread of the virus that should be included. The implementation of DepEd programs, projects, and activities was intensified and heightened because of the pandemic. The school community showed full participation, but teachers had experienced difficulty in implementing these because of some hindering factors such as limited resources and facilities. Furthermore, human resource development is not fully maximized as there are respondents who attended the same average number of trainings before the pandemic as they are now. While most training is centered on Learning and Development, training has now become scarce because of COVID-19.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".