What Makes Education Research Impactful – Case Studies of Research Projects in Singapore
Bibliographic record
Abstract
This study aims to address the gap in understanding the impact arising from education research, researcher collaborations with stakeholders, and knowledge mobilization activities in Singapore. Eight cases of local research projects are used to understand the phenomenon of research impact in different context-specific settings. The findings reveal differing perceptions of impact among research users and researchers, and cohesion on the factors that contribute to research impact. Drawing from the findings, the authors propose three emerging principles that can enhance research impact efforts: a) frontloading the intended research impact, b) building mutualistic relationships, and c) co-constructing research. The findings and emerging questions from the study contribute to the growing body of scholarship to help researchers and stakeholders strengthen the research-practice-policy nexus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".