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Record W2093062308 · doi:10.1177/1356389012442445

A socio-political framework for evaluability assessment of participatory evaluations of partnerships: Making sense of the power differentials in programs that involve the state and civil society

2012· article· en· W2093062308 on OpenAlexaff
Hélène Laperrière, Louise Potvin, Ricardo Zúñiga

Bibliographic record

VenueEvaluation · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsGeneral partnershipCitizen journalismParticipatory evaluationPoliticsPublic relationsCivil societyParticipatory action researchPower (physics)State (computer science)Political sciencePublic administrationSociologyComputer science

Abstract

fetched live from OpenAlex

Jointly conducted with a coalition of HIV/AIDS community-based organizations (CBOs), this evaluability assessment sought to better understand the factors that affect the feasibility of a participatory program evaluation to be undertaken in partnership with the CBOs’ non-governmental-organization members and public-health agencies. Participatory evaluations and partnerships are grounded in social and institutional authority structures that unavoidably influence researchers and evaluators. The construction of a theoretical framework for socio-political evaluability assessment of participatory evaluations is a necessary precondition for the coalition’s members to engage effectively in evaluation research with other partners.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.368
metaresearch head score (Gemma)0.361
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.368
Threshold uncertainty score0.780

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3680.361
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0160.006
Science and technology studies0.0080.033
Scholarly communication0.0130.011
Open science0.0030.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.568
GPT teacher head0.589
Teacher spread0.021 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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

Citations12
Published2012
Admission routes1
Has abstractyes

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