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Record W2129050343 · doi:10.26522/brocked.v22i2.342

Global Connectedness and Global Migration: Insights from the International Changing Academic Profession Survey

2013· article· en· W2129050343 on OpenAlexafffundvenue
Michelle K. McGinn, Snežana Ratković, C.C. Wolhunter

Bibliographic record

VenueBrock Education Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsBrock University
FundersBrock University
KeywordsGlobalizationInternationalizationSocial connectednessImmigrationContext (archaeology)Higher educationPolitical scienceSociologyJob satisfactionPublic relationsPsychologySocial psychologyGeography

Abstract

fetched live from OpenAlex

The Changing Academic Profession (CAP) international survey was designed in part to consider the effects of globalization on the work context and activities of academics in 19 countries or regions around the world. This paper draws from a subset of these data to explore the extent to which academics are globally connected in their research and teaching, and the ways this connectedness relates to global migration. Across multiple measures, immigrant academics (i.e., academics working in countries where they were not born and did not receive their first degree) were more globally connected than national academics (i.e., those working in the countries of their birth and first degree). Global migration by academic staff is clearly a major contributor to the internationalization of higher education institutions, yet there was no evidence these contributions led to enhanced career progress or job satisfaction for immigrant academics relative to national academics. The international expertise and experience of immigrant academics may not be sufficiently recognized and valued by their institutions.

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)0.009
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.338
Teacher spread0.316 · 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

Citations2
Published2013
Admission routes3
Has abstractyes

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