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Record W7161976857 · doi:10.82308/44099

Investigating the transformative value of learning experiences abroad in a summer field study program

2019· dissertation· en· W7161976857 on OpenAlexaboutno aff
Haoming Tang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningNarrativeQualitative researchContext (archaeology)Study abroad

Abstract

fetched live from OpenAlex

Selon une croyance populaire, les études à l'étranger apportent un large éventail d'avantages à ses participants. Dans le contexte de l'enseignement supérieur canadien, les résultats de la participation à des études à l'étranger pour la formation et la transformation de l'identité de l'étudiant n'ont pas été suffisamment étudiés. Utilisant principalement la théorie de «transformative learning» comme objectif théorique, cette étude narrative qualitative vise à comprendre l'expérience vécue par les étudiantes canadiennes qui étudient à la Barbade dans le cadre d'un programme expérimental d'été. Les données de recherche sont recueillies au moyen d'une série d'entretiens menés individuellement avec un total de quatre participants avant, pendant et après leur voyage d'étude à l'étranger. Les résultats montrent qu'au travers des expériences d'apprentissage à l'étranger, un certain degré de transformation s'est produit dans les dimensions contextuelle, intrapersonnelle et interpersonnelle de l'identité des participants. En particulier, ils ont démontré une meilleure compréhension de soi en passant par le processus de «experimental transformation». Les résultats de cette étude peuvent guider les organisateurs et les coordinateurs des études à l'étranger pour améliorer le dessein des programmes.

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)0.006
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.024
GPT teacher head0.395
Teacher spread0.371 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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