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Record W2796946910 · doi:10.17483/2368-6669.1129

Valuing Curriculum Evaluation as Scholarship: A Process of Developing a Community of Scholars

2018· article· en· W2796946910 on OpenAlexaffvenue
Betty Tate, Marilyn Chapman, Cheryl Zawaduk, Doris Callaghan

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsVancouver Island UniversityUniversity of British Columbia, Okanagan CampusThompson Rivers UniversityUniversity of British Columbia
Fundersnot available
KeywordsScholarshipCurriculumAccreditationSociologyPedagogyCurriculum developmentPolitical scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

Curriculum evaluation is an essential and complex activity intended to foster understanding of how teaching-learning practices serve to meet educational goals. This position paper provides a retrospective from the authors’ lived experience of one multi-institutional collaborative nursing programs’ history of curriculum evaluation and scholarship growth over 25 years. Key themes include an overview of collaborative curriculum evaluation underpinned by three philosophical perspectives, the influence of accreditation and re-mandated post-secondary institutions on scholarship development, an affirmation of curriculum evaluation as a form of knowledge development as supported by Boyer’s model of scholarship, and the development of scholarship expertise across collaborative partners through a focus on curriculum evaluation. Examples of curriculum evaluation practices are integrated throughout the paper. This retrospective review supports the contention that collaborative curriculum evaluation provides a strong foundation upon which to develop nursing education scholarship.

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)0.349
metaresearch head score (Gemma)0.333
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.349
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3490.333
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0130.007
Science and technology studies0.0250.048
Scholarly communication0.0390.026
Open science0.0060.049
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0030.001

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 head0.208
GPT teacher head0.586
Teacher spread0.378 · 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.

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

Citations0
Published2018
Admission routes2
Has abstractyes

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