Bibliographic record
Abstract
In this chapter I call for participatory monitoring and evaluation (M&E) of information and communication technology for development (ICT4D). I describe the ontology of ICT4D as complex and unpredictable. I favour an epistemology that is based on systems thinking and adaptive management as a foundation for participatory approaches. The M&E of ICTs faces a number of challenges including the lack of a unifying theoretical framework, the need to define users and purposes for each evaluation, the importance of agreeing on the type of causality that is expected, and the reality of short- term project durations. In response to these challenges I review established and emerging approaches such as Utilization Focused Evaluation, Outcome Mapping and Most Significant Change that embrace participation. Participation is a term open to many interpretations; to clarify its meaning I offer several ladders of participation. I conclude with a reflection on the conditions necessary for participatory approaches to gain acceptance in this field. A major lesson in participatory M&E is to understand the methods and to go beyond and be vigilant of the conditions that enable their application.Request access from your librarian to read this chapter's full text.
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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 | 0.069 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".