Competency-Based Learning for Future-Ready Governance: Functional and Behavioural Skills in Sarawak Local Councils
Bibliographic record
Abstract
The purpose of this study is to examine workforce competencies within Sarawak’s local councils and to explore how competency assessment can serve as an educational tool for Human Resource Development (HRD). Guided by Human Capital Theory, Strategic HRD, and Adult Learning principles, a mixed-methods design was employed combining survey data from 208 officers with four focus-group discussions and twelve semi-structured interviews. The principal results revealed a clear competency duality: behavioural competencies such as teamwork, cultural sensitivity, and communication scored higher (mean = 77.1%) than functional competencies (mean = 65.8%), where gaps were most pronounced in digital governance, crisis management, sustainability, and innovation. Qualitative findings elaborated on this disparity, identifying three recurring themes uneven digital and strategic proficiency, systemic barriers to continuous learning, and cautious optimism regarding future readiness and adaptability. The study concludes that integrating competency-based learning (CBL) within HRD frameworks is vital to cultivating a digitally literate, ethical, and future-ready workforce. Embedding CBL into HRD policy aligned with Malaysia’s Twelfth Plan (2021–2025), Sarawak’s PCDS 2030, and OECD’s Future-Ready Workforce recommendations can transform local councils into learning organisations capable of sustaining innovation and effective governance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".