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Record W1531534025 · doi:10.19173/irrodl.v6i1.218

Globalizing Flexible Work in Universities: Socio-technical dilemmas in internationalizing education

2005· article· en· W1531534025 on OpenAlexvenueno aff
Michael Singh, Jinghe Han

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
FundersYuncheng University
KeywordsDistance educationThe InternetHigher educationPopulationPublic relationsPoliticsGlobalizationWork (physics)SociologyPolitical scienceEconomic growthMarketingBusinessEconomicsPedagogyEngineeringWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

<p>We engage with and respond to the debate raised by this theme issue of the International Review of Research in Open and Distance Learning with a particular question in mind: namely, as universities are using new labor displacing technologies to export degrees to meet the international demand for higher education, how is this influencing – negatively and positively – the workers involved? Contemporary transitions in political and economic globalization are being used to press universities into becoming ‘transnational businesses,’ seemingly driven by a primary concern for marketing educational commodities. The neo-liberal politics driving these currents in universities are increasing the multiple online and offline networks. These local/ global meshworks engage the labors of a small but growing percentage of the world’s population (Singh, 2002, pp. 217-230). Writing this paper at Jilin University in China, we find that many of our academic colleagues and students have limited access to a personal desktop computer, the Internet, and email. They must pay for timed access to their email accounts and for downloading attachments. They do not have access to high-speed data networks. A timer indicates how long it will take to open and send emails. Around us, construction workers are building massive facilities to house the burgeoning on-campus student population. Their offline education is being supplemented – but not replaced by ever-advancing online technologies. </p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.478
Teacher spread0.404 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations20
Published2005
Admission routes1
Has abstractyes

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