Scholar/practitioner research in international development volunteering: benefits, challenges and future opportunities
Bibliographic record
Abstract
International development volunteering (IDV) is the practice of sending skilled international volunteers to exchange knowledge and skills with community-based organisations and individuals in a partner country. IDV is a popular form of development assistance in many countries. As the popularity of these programmes grows, so too does the need for – and interest in – better understanding of their impacts and dynamics. Scholar/practitioner research collaborations provide opportunities for improved knowledge development in this field of study. To better understand the dynamics of these collaborations, researchers collected survey data from 22 scholars and practitioners involved in IDV research, as well as notes from a workshop with 40 stakeholders from the IDV community. Thematic analysis of these data considers the distinctive features of collaboration models used in IDV research. Taken together, these data identify several benefits to collaboration and/or research partnerships as well as significant challenges that limit the scope and impact of their work. The findings from this study provide insights into opportunities for enhancing effective practices and designing new collaborative efforts for engaging in scholar/practitioner collaboration in IDV.
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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 | 0.090 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".