A Tour of Open Universities Through Literature
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".