Practice research’s challenges and opportunities across project conceptualisation, implementation, and dissemination: a Singaporean case study
Bibliographic record
Abstract
Because practice research’s benefits in social work contexts are well-documented, research attention has shifted to improving its operationalisation within organisations. However, few studies have examined practice research’s challenges and opportunities across project conceptualisation, implementation, and dissemination. Even fewer have considered practitioner-researcher and organisational power dynamics, especially in Asia. Using a qualitative exploratory case study approach, we first described the challenges a Singaporean youth work agency faced during conceptualisation, implementation, and dissemination across three practice-research projects. Subsequently, we evaluated how the core practice-research team seized opportunities to address these challenges (i.e. manpower and resourcing, disproportionate researcher influence, sustaining interest internally and externally) and future improvement opportunities (i.e. institutionalise knowledge, build organisational capacity, and examine structural impediments). Implications for practitioners and researchers and effective organisational strategies – internally, externally, and structurally – are discussed.
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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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".