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Record W2989812061 · doi:10.19173/irrodl.v20i3.4079

A Tour of Open Universities Through Literature

2019· article· en· W2989812061 on OpenAlexvenueno aff
Francisco Javier Hinojo Lucena, Inmaculada Aznar Díaz, María Pilar Cáceres Reche, José María Romero Rodríguez

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsBespokePhenomenonHigher educationOpen educational resourcesBibliometricsWork (physics)Sample (material)Open educationSociologyPublic relationsPolitical scienceSocial scienceLibrary sciencePedagogyComputer scienceEconomic growthEngineeringEconomics

Abstract

fetched live from OpenAlex

Since open universities can adapt to students’ work schedules, personal preferences, age, and so on, they have facilitated access to education for a large group of people. The open university phenomenon, which arose in the 1960s, fostered countries’ cultural growth; higher education was now longer exclusively for a privileged few. This paper presents a bibliometric study on the existing scientific output on open universities throughout the last 40 years. A bespoke methodology of bibliometric studies has been used, by setting key descriptors to be consulted in the most prestigious scientific database Web of Science. The sample was composed of 809 papers in total, indexed in prestigious journals and published during the period 1969 to 2018. The output, scattering, and impact bibliometric indicators were analysed in those papers. Among the results obtained, it was found that scientific output on open universities is in a phase of exponential growth, in which 2015 was the most productive year. Furthermore, the UK, where the phenomenon of open universities started, is the country with the most scientific output. Finally, the bibliometric study of the output indexed in the Web of Science shows a panoramic vision of the past, present, and future of open universities, emphasising the idea that this phenomenon is continuing to grow.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.003
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 head0.061
GPT teacher head0.446
Teacher spread0.385 · 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 designTheoretical or conceptual
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

Citations11
Published2019
Admission routes1
Has abstractyes

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