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Record W2112793242 · doi:10.5539/ies.v5n5p226

Future Challenges in Higher Education - Bologna Experts' Community Case Study

2012· article· en· W2112793242 on OpenAlexvenueno aff
Miri Yemini

Bibliographic record

VenueInternational Education Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsBologna ProcessHigher educationEuropean unionDocumentationPolitical scienceScope (computer science)Work (physics)CommissionEuropean commissionComparative educationPublic administrationPublic relationsEconomic growthBusiness

Abstract

fetched live from OpenAlex

This work presents results from systematic analysis of the challenges for the future of higher education in European and neighboring countries as it was extracted from the Bologna experts and Higher Education Reform experts' opinions. Opinions of more than 100 experts from 35 countries were documented and analyzed. Significant differences in the opinions on future challenges in higher education were found to be addressed by opinions of experts from European Union in comparison to neighbouring countries. Differences were recorded in the amount and scope of challenges presented by experts from different countries and also the data collected differs significantly from the official policy for future development as reflects from official documentation of the European Commission. This work provides an insight into the bottom up implementation of the Bologna process and it can be useful for academics and policymakers in the field of higher education.

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)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0070.003
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.229
GPT teacher head0.474
Teacher spread0.245 · 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 designQualitative
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

Citations6
Published2012
Admission routes1
Has abstractyes

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