Assessing innovations in international research and development practice
Bibliographic record
Abstract
Enhancing impacts of international development interventions has become a central issue of the twenty-first century.Conventional monitoring and evaluation (M&E) tools either focus on efficiency (output-to-input relationships) or strive to demonstrate a logical progression from specific actors and factors of an intervention to development impacts (inputs => activities => outputs => outcomes => impacts).However, in complex adaptive systems there is neither such a linear results chain nor can impacts be unambiguously attributed to an actor or a factor.Therefore, alternative ways of doing M&E focus on outcomes -the changes in behaviour and social relations -rather than on impacts, such as poverty reduction, environmental protection and social inclusion.Innovation systems thinking, particularly in renewable natural resource, agriculture and rural development, informs that the dominant paradigm of impact assessment should be complemented by social innovation assessment, providing research and development actors with critical learning lessons.This paper integrates two distant bodies of literature -the literature on impact assessment of research and development interventions, and the literature on social psychology of assessing learning and innovations.Based on case studies of a series of projects implemented in India and Nepal under DFID's 11-year Renewable Natural Resources Research Strategy (RNRRS) programme between 1995 and 2006, a social innovation assessment tool was developed and implemented.The tool includes questions about critical incidents and modes of stakeholder interactions to be ranked on a four-point scale depending on how often the statements apply to the respondents' work environments.The social innovation assessment provides critical learning lessons for social innovation generation and overall performance improvement in collaborative research and development interventions at the organisational, network and system levels.
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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 | 0.283 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".