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Record W4366449610 · doi:10.3138/cjpe.22.001

Methodological and Conceptual Challenges in Studying Evaluation Process Use

2007· article· en· W4366449610 on OpenAlexvenueno aff
Sandy Taut

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationProcess (computing)Construct (python library)Context (archaeology)Exploratory researchProcess managementQuality (philosophy)Knowledge managementManagement scienceComputer scienceQualitative researchConceptual modelScale (ratio)PsychologySociologyEpistemologyBusinessEngineering

Abstract

fetched live from OpenAlex

Abstract: This article discusses methodological and conceptual challenges in empirically studying process use. The main difficulty lies in disentangling cause (here the evaluation process) and effects (here indicators of process use). The evaluation researcher not only needs to take into account all relevant factors regarding the evaluator, the participants, the evaluation context, and the evaluation approach and implementation, but also needs to base the research on a valid operationalization of process use. The article was inspired by the author’s experiences in conducting an exploratory study of process use in the context of two expert-facilitated self-evaluation projects involving five program staff. Before larger-scale studies can establish more generalizable knowledge on process use, evaluation researchers should engage in high-quality, real-time, in-depth qualitative studies to better understand the complex interactions at play and to help build a solid operationalization of this relevant construct.

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
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
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)0.115
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1150.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.932
GPT teacher head0.660
Teacher spread0.272 · 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 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Methods

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

Citations6
Published2007
Admission routes1
Has abstractyes

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