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Record W2319861733 · doi:10.1177/1098214014542100

Insights on Using Developmental Evaluation for Innovating

2014· article· en· W2319861733 on OpenAlex
Chi Yan Lam, Lyn M. Shulha

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Evaluation · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsQueen's University
Fundersnot available
KeywordsConceptualizationProcess (computing)Knowledge managementProcess managementRendering (computer graphics)Computer sciencePsychologyManagement scienceBusinessEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This article contributes to research on evaluation by examining the capacity and contribution of developmental evaluation for innovating. This case study describes the preformative development of an educational program (from conceptualization to pilot implementation) and analyzes the processes of innovation within a developmental evaluation framework. Developmental evaluation enhanced innovation by (a) identifying and infusing data primarily within an informing process toward resolving the uncertainty associated with innovation and (b) facilitating program cocreation between the clients and the developmental evaluator. Analysis into the demands of innovation revealed the pervasiveness of uncertainty throughout development and how the rendering of evaluative data helped resolve uncertainty and propelled development forward. Developmental evaluation enabled a nonlinear, coevolutionary program development process that centered on six foci—definition, delineation, collaboration, prototyping, illumination, and reality testing. This article concludes by encouraging evaluators to understand the demands of innovation and the value of design thinking when innovating.

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.

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)0.029
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.011
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)0.0000.000

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 head0.357
GPT teacher head0.554
Teacher spread0.197 · 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