Building Spaces for Dialogues to Rethink Evaluator Competencies: Lessons from the Webinars Organized by the Evaluation Centre for Complex Health Interventions
Bibliographic record
Abstract
Background: There is a need to rethink evaluator competencies given the harsh and paralyzing realities of COVID. The pandemic was a time where there was a need to balance diverse perspectives given the limited scientific evidence that existed when faced with a genuinely unprecedented time. In the Fall of 2021 (September to October), the Evaluation Centre for Complex Health Interventions in partnership with the Asia Pacific Evaluation Association organized a three-part webinar series in response to the multiple issues that surfaced during COVID-19, and specifically, the implications of the pandemic for rethinking evaluator competencies and evaluator training. The presenters were from multiple countries including India, Canada, USA, UK, and South Africa. Purpose: The presenters pushed for more responsive evaluation approaches to address inequities and sustainability and for a decolonized approach to knowledge building. The webinar raised a number of themes that have potential implications for future discussions on evaluator competencies including: enhancing evaluation contributions to the Sustainable Development Goals (SDGs), the need to rethink evaluation criteria, the need to embrace and address varieties of uncertainties, focus on diversity and heterogeneity; understanding the role of contexts in complex programs and policies; the need to reconceptualize sustainability; being more explicit about inequities and vulnerabilities; and the need to pay attention to systems and system dynamics. Setting: The webinars were organized by the Evaluation Centre and the Asia Pacific Evaluation Association on a Zoom platform. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: Not applicable. Findings: Not applicable.
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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 | 0.280 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".