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Social constructs of curricular change

2000· article· en· W2340467347 on OpenAlexaff
Shafik Dharamsi, D. Christopher Clark, M A Boyd, D D Pratt, Bevan Craig

Bibliographic record

VenueJournal of Dental Education · 2000
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumResistance (ecology)Medical educationFaculty developmentPsychologyProcess (computing)Social changePlan (archaeology)Qualitative researchDental educationPedagogyProfessional developmentMedicineSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The adoption of problem-based approaches to teaching and learning in dental and medical education requires educators to consider a significantly different role and responsibilities as teacher from what they have experienced previously. This qualitative study explored how some educators experienced and interpreted changes in the newly merged dental and medical curriculum at the University of British Columbia. Our findings present how educators explained and dealt with change. In-depth interviews provided considerable insight into factors influencing the resistance or acceptance to change. The educators' beliefs about teaching and learning and their understanding of the development and implementation process of change mediated these factors. Findings from this study should help administrators, faculty developers, and educators themselves to understand better how curricular change is experienced and to plan effective and appropriate faculty development.

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)0.011
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
Science and technology studies0.0090.053
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.364
Teacher spread0.346 · 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 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

Citations9
Published2000
Admission routes1
Has abstractyes

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