Bibliographic record
Abstract
Innovation is essential in addressing complex evaluation capacity building (ECB) efforts that include a host of interacting, nonlinear, adaptive, and dynamical individual and organizationallevel factors. This article highlights five key ingredients in fostering innovation in ECB, based on evaluation capacity building efforts of the Ontario Centre of Excellence for Child and Youth Mental Health. For the past 5 years, 87 organizations have participated in an integrated ECB program combining funding, training, and coaching support. The five key ingredients to fostering innovation in ECB are curiosity, courage, communication, commitment, and connection. L’innovation est essentielle pour aborder les efforts complexes de renforcement et de developpement des capacites d’evaluation (RCE) qui comprennent une gamme de facteurs uniques et organisationnels en interaction, facteurs non lineaires, adaptables, et dynamiques. Cet article presente cinq ingredients cles pour favoriser l’innovation dans le RCE bases sur les efforts de renforcement des capacites d’evaluation du Centre de l’excellence de l’Ontario en sante mentale des enfants et des adolescents. Depuis 5 ans, 87 organisations ont participe a un programme RCE associant le financement, la formation, et les services d’accompagnement. Les cinq principaux ingredients pour favoriser l’innovation dans le RCE sont la curiosite, le courage, la communication, l’engagement, et la connexion.
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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 | 0.172 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.012 | 0.018 |
| 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".