Advancing a Model for Enhancing Research Competencies among Non-Academic Staff in Northeast Thailand Higher Education Institutions
Bibliographic record
Abstract
The development of research competency among non-academic personnel in higher education institutions is a crucial endeavor that aligns with the evolving demands of the 21st-century workforce. This study employs a comprehensive research and development approach to create an advanced model for enhancing research competencies encompassing knowledge, skill, and attitude. The model's design is informed by meticulous need analysis, ensuring its relevance to the unique challenges faced by non-academic staff. Through expert evaluation, the model's efficacy is demonstrated in improving research-related capacities. The evaluation results underscore its robustness across various dimensions, with significant improvements observed in participants' research competencies. This study highlights the interconnectedness of knowledge, skill, and attitude in fostering research competency and supports the broader view that tailored interventions, derived from thorough need analysis, play a pivotal role in driving meaningful and sustainable improvements in research-related skills and capabilities. Ultimately, this research contributes to the ongoing discourse on non-academic staff empowerment and the advancement of higher education institutions in an increasingly research-focused landscape.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".