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Record W3138747072

Strategic approaches to glocalising curriculum practice: Responding to faculty development needs and circumstances in diverse university contexts: Strategic approaches to glocalising curriculum practice

2021· article· en· W3138747072 on OpenAlexaff
Andrea S. Webb, Harry Hubball, Meriem McKenzie

Bibliographic record

VenueInternational Journal of Curriculum and Instruction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumScholarshipPedagogySociologySituatedFaculty developmentContext (archaeology)Curriculum developmentInclusion (mineral)Public relationsPolitical scienceProfessional developmentDiversity (politics)Engineering ethicsSocial scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

University campuses around the world face significant challenges for engaging culturally diverse faculty and students with responsive programming (e.g., undergraduate, graduate, staff and faculty development programs). Policy documents espousing inclusion and the strategic institutional importance of local and global engagement, for example, are positive steps to foster institutional change. However, in practice, ad-hoc curricula renewal initiatives aimed at facilitating cultural diversity tend to be far less strategic, and with scant attention to research-informed and evidence-based scholarship. This paper attempts to address these complex challenges and provides insights toward a scholarly approach to glocalising curriculum practice for faculty development in multinational settings. In this context, data suggests that strategic institutional supports are key to glocalising curriculum practices. Further, a glocalised curriculum is inherently situated; socially and culturally mediated; and, is responsive to the professional learning needs and circumstances of educational leaders in diverse institutional contexts.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.602
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.109
GPT teacher head0.335
Teacher spread0.226 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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