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Record W4401364631 · doi:10.3138/cjpe-2023-0020

Developing an Evaluation Training Program for Community-Based Organizations: A Participatory Curriculum Development Approach

2024· article· en· W4401364631 on OpenAlexaffvenue
David Buetti, Isabelle Bourgeois

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTraining (meteorology)CurriculumCitizen journalismCurriculum developmentMedical educationSociologyComputer sciencePolitical sciencePedagogyGeographyMedicine

Abstract

fetched live from OpenAlex

Community-based organizations increasingly recognize the value of evaluative insights in informing decision-making and improving their services. However, these organizations often lack the capacity to conduct and use evaluations, and traditional evaluation capacity building (ECB) activities are not always adapted to their particular contexts. This practice note introduces an innovative evaluation training program for small grassroots community-based organizations. Employing a participatory curriculum development approach, the training program was tailored to meet the specific needs and realities of the community sector. The authors first outline the collaborative process used to involve community and academic stakeholders in the program’s development. They then present the resulting training plan, consisting of seven self-directed online learning modules with chosen activities to foster learning and knowledge transfer into practice. The potential implications for designing, researching, and evaluating similar initiatives are explored and discussed.

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)0.054
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.693
GPT teacher head0.577
Teacher spread0.116 · 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; a candidate call from one teacher head, not a consensus.

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

Citations5
Published2024
Admission routes2
Has abstractyes

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