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Record W1767493202

The 5 Cs for innovating in evaluation: Lessons from the field

2013· article· en· W1767493202 on OpenAlexaffvenueabout
Evangeline Danseco

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental Health
Fundersnot available
KeywordsExcellenceCourageCoachingHumanitiesPolitical scienceSociologyManagementArtEconomics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.529
GPT teacher head0.595
Teacher spread0.066 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes3
Has abstractyes

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