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Record W4366675251 · doi:10.3138/cjpe.019.005

An Empirical Study of Building the Evaluation Capacity of K–12 Site-Managed Project Personnel

2004· article· en· W4366675251 on OpenAlexvenueno aff
Paul R. Brandon, Terry Ann F. Higa

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2004
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)PsychologyMedical educationProgram evaluationProfessional developmentEvaluation methodsCapacity buildingApplied psychologyPedagogyPolitical scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Abstract: This article examines the effects of professional development, including formal workshops and ongoing consultation, on the evaluation capacity of K–12 school faculty and administrators who were conducting evaluations of 17 site-managed projects. Changes in the faculty’s and administrators’ (a) attitudes toward evaluation, (b) self-confidence as evaluators, and (c) assessments of their capabilities as evaluators were examined. School personnel’s attitudes toward evaluation did not improve, but their self-confidence as evaluators and their assessment of their evaluation capabilities both showed improvement. The conclusions buttress the argument that, with training and the assistance of experienced evaluators, school personnel can build their evaluation capacity. A number of limitations in study design and data are noted.

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)0.026
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.063
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.593
GPT teacher head0.566
Teacher spread0.027 · 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.

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

Citations5
Published2004
Admission routes1
Has abstractyes

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