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Record W1599028796 · doi:10.1108/00483480310498710

International experience and academic careers

2003· article· en· W1599028796 on OpenAlexaff
Julia Richardson, Steve McKenna

Bibliographic record

VenuePersonnel Review · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsInternationalizationContext (archaeology)Academic mobilityPublic relationsKey (lock)Higher educationInternationalization of Higher EducationSociologyInternational educationCareer developmentManagementMarketingPolitical scienceBusinessPedagogyEconomics

Abstract

fetched live from OpenAlex

This paper focuses on the relatively unexplored link between international experience and academic careers. Drawing on a study of 30 British academics in four countries, it reports how they accounted for their decision to take an overseas appointment and how they evaluated that appointment. The contemporary career literature is used as a framework for analysis connecting the findings with “traditional” and “new” career themes. The desire to travel was found to be a key driver in taking the overseas appointment. When it came to evaluating the overseas appointment, however, upward career mobility in the context of increasing internationalisation was a major concern. The paper offers a number of key concerns for managers in institutions of higher education, particularly those concerned with the management and recruitment of international faculty.

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)0.006
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.084
GPT teacher head0.399
Teacher spread0.315 · 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

Citations148
Published2003
Admission routes1
Has abstractyes

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