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Record W4393901512 · doi:10.3138/cjpe-2024-0002

Evaluation Literacy and Use in Community Organizations: Insights and Implications for Evaluation Capacity Building

2024· article· en· W4393901512 on OpenAlexaffvenue
David Buetti, Isabelle Bourgeois, Sébastien Savard

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Ottawa
FundersAustralian Government
KeywordsCapacity buildingLiteracyBusinessCapacity developmentEnvironmental planningEnvironmental resource managementSociologyEconomic growthEconomicsPedagogyGeography

Abstract

fetched live from OpenAlex

Past research on evaluation capacity in community organizations has focused on their capacity to conduct rather than use evaluation. This study sought to address this gap by exploring evaluation use and the supporting processes, systems, and structures in the community sector. The authors administered an online survey to 102 individuals working or involved in community organizations to measure their perceptions of their organization’s capacity to use evaluation. The survey was based on an adaptation of Bourgeois and Cousins’ evaluation capacity framework and a related assessment tool, the Organizational Evaluation Capacity Assessment Instrument, for the community sector context. Their findings offer empirical insights into three critical dimensions of the adapted framework associated with evaluation use in community organizations: evaluation literacy, organizational decision-making, and learning benefits. This exploratory study generated several key findings, including the need for tools and policies that establish a strong link between evaluation and decision-making, expanding the scope of engagement in data interpretation beyond primary users, and extending the dissemination of findings to external stakeholders. Implications for practice and research are 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.046
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0000.000
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 head0.432
GPT teacher head0.540
Teacher spread0.108 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2024
Admission routes2
Has abstractyes

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