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Record W6927452472 · doi:10.25777/45rt-yp87

Research to Develop a Consensus Self-Evaluation Model of National Norms of Excellence for Alternating Cooperative Education Programs at Four-Year Colleges and Universities

2019· article· en· W6927452472 on OpenAlexfundno aff

Bibliographic record

VenueODU Digital Commons (Old Dominion University) · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
FundersConcordia University of EdmontonOld Dominion UniversityU.S. Department of Education
KeywordsExcellenceCooperative educationDelphi methodDelphiHigher educationField (mathematics)

Abstract

fetched live from OpenAlex

The research described in this dissertation was conducted in response to an expressed need for the development of national norms of excellence for cooperative education programs in the United States in 1980. In academic year 1981-1982 a Delphi technique was used with 12 cooperative education experts, who identified 155 cooperative education program norms of excellence specifically for four-year alternating cooperative education programs. In academic year 1982-1983, the 155 norms identified were transposed into a 90-item self-evaluation questionnaire which was field tested and sampled at 14 colleges and universities with alternating cooperative education programs in the United States. Of 900 college administrators, faculty, cooperative education coordinators, students and employers contacted, 730 responded (81%). The alternating cooperative education program consensus self-evaluation model developed was the first of its kind in the United States. With further refinement and testing it could be adapted for use by other cooperative education programs. Appendices include directions for conducting a Delphi Technique, directions for conducting cooperative education program self-evaluation, anecdotal comments from respondents, and definitions of cooperative education provided by respondents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.145
GPT teacher head0.392
Teacher spread0.247 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

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