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Record W2563458459 · doi:10.5206/cie-eci.v45i3.9297

Multi-stakeholder Partnership in Teacher Education and Development

2016· article· en· W2563458459 on OpenAlexaffvenue
Linyuan Guo-Brennan, Carolyn Francis, Elizabeth Townsend, Michael Guo-Brennan

Bibliographic record

VenueComparative and International Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsPracticumKenyaGeneral partnershipStakeholderContext (archaeology)Teacher educationProfessional developmentPedagogyService (business)Political sciencePublic relationsSociologyBusinessGeographyMarketing

Abstract

fetched live from OpenAlex

While there is a growing interest in offering international teaching practicums to pre-service teachers as an approach to developing teachers' global perspectives on education, however, understanding of the impact of such practice on the host communities is insufficient or absent. This study collected data from multiple sources and, using the Theory of Change framework, examined the impact of pre-service teachers' international practicum on the school and community development in Kenya. Through exploring the context of host communities, the perceptions of Kenyan educators, and the changes that have occurred in Kenyan schools and communities, this study revealed positive development changes in Kenyan schools as the result of hosting pre-service teachers. A model of forming multi-stakeholder global partnership in higher education was presented to make the international teaching practicum a reciprocal professional development opportunity for educators in both sending and receiving countries.

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)0.033
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.014
Scholarly communication0.0090.008
Open science0.0010.013
Research integrity0.0030.003
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 head0.308
GPT teacher head0.464
Teacher spread0.156 · 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 designObservational
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
Published2016
Admission routes2
Has abstractyes

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