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Record W2727038118 · doi:10.3138/cjpe.31039

“Advocates Change the World; Evaluation Can Help”: A Literature Review and Key Insights from the Practice of Advocacy Evaluation

2017· review· en· W2727038118 on OpenAlexvenueaboutno aff
Juniper Glass

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2017
Typereview
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical sciencePublic relationsWork (physics)Theory of changeResource (disambiguation)Value (mathematics)Public administrationSociologyComputer science

Abstract

fetched live from OpenAlex

Abstract: It is only since the new millennium that assessments of policy and systems change initiatives have been given much attention in practice or in research. A comprehensive literature review of advocacy in human services non-profit organizations (NGOs) has found a “lack of systemic, rational evaluation and measurement of the effectiveness of advocacy.” Similarly, in a survey of 211 NGOs that undertake advocacy, only one in four (24.6 percent) reported that this work had been evaluated. While the survey was conducted in the United States, it is likely that Canadian NGOs are in a similar situation, not because of a lack of interest or value for evaluation but, rather, because assessing systems change initiatives is challenging territory and NGOs face considerable resource and time constraints that put such evaluation low on the priority list. This article provides a review of key insights from advocacy evaluation practice and research that may help orient and inform NGOs as they decide on evaluation strategies. I will outline the state of the field of systems- and policy-change evaluation in North America as well as its benefits and challenges. Finally, a synthesis of the main steps in advocacy evaluation planning and implementation offers a broad map to NGOs seeking to enhance this practice in their own organizations.

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)0.073
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0730.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.569
GPT teacher head0.599
Teacher spread0.030 · 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
GenreReview

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
Published2017
Admission routes2
Has abstractyes

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