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Record W1591702131 · doi:10.3138/cjpe.28.002

To Case Study or Not to Case Study: Our Experience with the Canadian Government’s Evaluation Practices and the Use of Case Studies as an Evaluation Methodology for First Nations Programs

2013· article· en· W1591702131 on OpenAlexvenueaboutno aff
Andrea L. K. Johnston

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Case study researchWork (physics)Public relationsComparative caseValue (mathematics)Political sciencePsychologyBusinessMarketingEngineering

Abstract

fetched live from OpenAlex

Abstract: Canadian policy decision-making has utilized case studies extensively in recent years. Johnston Research Inc. (JRI) has completed more evaluation-related case studies over the past 4 years than in the previous 15 years of our evaluation work. To understand the growing application of case studies, we interviewed clients and contacts from First Nations that had been case study sites for our government clients, to understand what aspects of case study evaluation research had helped them share their opinions and improve their programs, and what aspects had not. We then interviewed our government clients, asking how well case studies served their evaluation purposes and their programs or policy development efforts. JRI conducted and financed this study to help us improve our own approaches for conducting case studies in Aboriginal populations and to share these findings with others. This article presents our interview findings on the value of case studies for Aboriginal evaluation projects and shares some best practices for conducting case studies within, and with, First Nations. Finally, we explore the impact case studies have had on Canadian policy.

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)0.070
metaresearch head score (Gemma)0.052
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0700.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.002
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 head0.853
GPT teacher head0.655
Teacher spread0.198 · 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 designQualitative
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

Citations7
Published2013
Admission routes2
Has abstractyes

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