Evaluating the Impacts of Media Assistance: Problems and Principles
Bibliographic record
Abstract
While some form of evaluation has always been a requirement of development projects, in the media assistance field this has predominantly been limited to very basic modes of counting outputs, such as the number of journalists trained or the number of articles produced on a topic. Few media assistance evaluations manage to provide sound evidence of impacts on governance and social change. So far, most responses to the problem of media assistance impact evaluation collate evaluation methodologies and methods into toolkits. This paper suggests that the problem of impact evaluation of media assistance is understood to be more than a simple issue of methods, and outlines three underlying tensions and challenges that stifle implementation of effective practices in media assistance evaluation. First, there are serious conceptual ambiguities that affect evaluation design. Second, bureaucratic systems and imperatives often drive evaluation practices, which reduces their utility and richness. Third, the search for the ultimate method or toolkit of methods for media assistance evaluation tends to overlook the complex epistemological and political undercurrents in the evaluation discipline, which can lead to methods being used without consideration of the ontological implications. Only if these contextual factors are known and understood can effective evaluations be designed that meets all stakeholders' needs.
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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 | 0.356 | 0.386 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.007 | 0.088 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| 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".