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Record W1995242301 · doi:10.5539/ass.v11n10p292

University Rankings as a Tool for Assessing the Quality of Education in the Context of Globalization

2015· article· en· W1995242301 on OpenAlexvenueno aff
Никита Владимирович Авралев, Irina Efimova

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityGlobalizationContext (archaeology)Higher educationQuality (philosophy)Process (computing)ContradictionAdaptabilityDoctrinePolitical scienceBusinessEconomic growthEconomic systemEconomicsManagementComputer scienceLaw

Abstract

fetched live from OpenAlex

Article is devoted to the new conditions for the development of society characterized by the reconstruction of the higher education and problem of increasing the competitiveness of Russian universities in the world. Global university rankings today are becoming indicators of the implementation of the integration process and competitive tool in the context of globalization of higher education. A characteristic feature of modern development is the transition to a new stage of the formation of an innovative society, to build an economy based on the generation, distribution, transfer and use of knowledge. Ability to adapt capacity to the constantly changing environment is becoming the leading trend, the main source of material prosperity of civil society. And university rankings as indicators and tools of the competitiveness of universities certainly play an increasingly important role in the interaction of universities, businesses and states in the global educational space. Modern period of development in Russia clearly identified the need to update the main priorities in the field of education in line with global trends. One such leading priority, as the quality of education found expression in national doctrine of Russian education. This circumstance is dictated by the presence of the basic contradiction between the modern requirements for quality of higher education and restrictions apply methods and technologies in the management process. Designing an effective system of quality management education is determined by a number of conditions and factors that create discomfort or provide adaptability alternatively.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.410
Teacher spread0.368 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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