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Record W3153692697 · doi:10.5539/hes.v11n2p155

Program Specific Effects of a Semester Abroad on the Likelihood of Pursuing a PhD

2021· article· en· W3153692697 on OpenAlexvenueno aff
Laura Urgelles

Bibliographic record

VenueHigher Education Studies · 2021
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPropensity score matchingPsychologyHigher educationStudy abroadSample (material)Matching (statistics)GermanMathematics educationMedical educationPedagogyStatisticsPolitical scienceMathematicsMedicine

Abstract

fetched live from OpenAlex

The present paper provides an analysis of the impact of a semester abroad during university studies on a students’ likelihood of pursuing a PhD. I use a sample of 66 812 German university students and analyze the program-specific subsamples. Propensity score matching reveals that business students who go abroad during their studies have higher intentions to pursue a PhD than their non-mobile peers. The findings are robust across matching estimators. In addition, I find positive and significant effects for cultural and social studies, whereas the effects for medical and law students are insignificant. When splitting the sample at the median grade, a semester abroad has a significant positive impact on below-median grade natural sciences students’ PhD decision. In contrast, for engineering students there is a positive and significant effect of a semester abroad only for above-median performers. I build on existing findings concerning the existence of a positive correlation between mobility and the intention to pursue a PhD.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.194
GPT teacher head0.474
Teacher spread0.280 · 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.

Study designObservational
DomainIncentives
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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