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Record W2913743877 · doi:10.7202/1057127ar

The Relationship Between Experiential Learning and Career Outcomes for Alumni of International Development Studies Programs in Canada

2019· article· en· W2913743877 on OpenAlexafffundvenueabout
Rebecca Tiessen, Kate Grantham, John Cameron

Bibliographic record

VenueCanadian Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsMcGill UniversityDalhousie UniversityUniversity of Ottawa
FundersInternational Development Research Centre
KeywordsExperiential learningPerspective (graphical)Career developmentPsychologyField (mathematics)Graduate studentsExperiential educationWork (physics)Pedagogy

Abstract

fetched live from OpenAlex

In this paper, we explore the relationship between experiential learning and career outcomes for international development studies (IDS) graduates from the perspective of program alumni, by presenting the results of a national survey completed by 1,901 IDS alumni across Canada. Employing study data, we answer the following research questions: (1) What do IDS alumni consider important experiential learning opportunities? and (2) What is the perceived relationship between experiential learning and career outcomes? We argue that documenting IDS graduate perspectives on the relationship between experiential learning and career paths can inform current program opportunities and highlight the relationship between work-integrated learning and career success in this field.

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)0.002
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.337
Teacher spread0.272 · 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

Citations14
Published2019
Admission routes4
Has abstractyes

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