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Record W3034762892 · doi:10.3138/jvme.2019-0125

Leading Significant Institutional Change in the Context of an OIE-Endorsed Veterinary Twinning Project

2020· article· en· W3034762892 on OpenAlexvenueno aff
Lachlan McIntyre, T. J. Parkinson

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumContext (archaeology)GrassrootsPolitical scienceVeterinary medicinePublic relationsMedical educationPoliticsSociologyMedicinePedagogyGeography

Abstract

fetched live from OpenAlex

A 5-year World Organisation for Animal Health Veterinary Twinning Program between Massey University, New Zealand, and the University of Peradeniya, Sri Lanka, was initiated in 2014. The key aims of the project were renewal of the curriculum, rejuvenation of teaching methodology, and creation of a platform for sustainable clinical and extension livestock services within the teaching program. The project succeeded in facilitating the development of a new veterinary undergraduate curriculum that was based upon student-centered and problem-based approaches to teaching and learning. Key reasons for the success of the project were (a) perceptions for the necessity of changes at the University of Peradeniya; (b) the management of expectations of both partners in the program along with their key stakeholders; (c) allowing sufficient time (i.e., 5 years) for agreement, establishment, and implementation of the changes; and (d) the development of the relationships of trust between faculty of the partner institutions at both decision-making and grassroots levels. From a project management perspective, the project required bringing about significant change in another organization, in a foreign country, and with a distinctly different culture. Moreover, notwithstanding the value of a long project, project managers should be prepared for significant political, organizational, and personnel change over the duration of such a project.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.625
GPT teacher head0.571
Teacher spread0.054 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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