Developmental evaluation during the COVID-19 pandemic: Practice-based learnings from projects in British Columbia, Canada
Bibliographic record
Abstract
In this article, we explore experiences and learnings from adapting to challenges encountered in implementing three Developmental Evaluations (DE) in British Columbia, Canada within the evolving context of the COVID-19 pandemic. We situate our DE projects within our approach to the DE life cycle and describe challenges encountered and required adaptations in each phase of the life cycle. Regarding foundational aspects of DEs, we experienced challenges with relationship building, assessing and responding to the context, and ensuring continuous learning. These challenges were related to suboptimal embeddedness of the evaluators within the evaluated projects. We adapted by leveraging online channels to maintain communications and securing stakeholder engagement by assuming non-traditional DE roles based on our knowledge of the context to support project goals. Additional challenges experienced with mapping the rationale and goals of the projects, identifying domains for assessment, collecting data, making sense of the data and intervening were adapted to by facilitating online workshops, collecting data online and through proxy evaluators, while sharing methodological insights within the evaluation team. During evolving crises, like the COVID-19 pandemic, evaluators must embrace flexibility, leverage, and apply their knowledge of the evaluation context, lean on their strengths, purposefully reflect and share knowledge to optimise their DEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.000 |
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; both teacher heads 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".