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Complex Activity & Selective Associativity: the Effectiveness of Elite University Association

2022· article· en· W4319594685 on OpenAlexaboutno aff
Volodymyr Lugovyi, Olena Slyusarenko, Жаннета Таланова

Bibliographic record

VenueInternational Scientific Journal of Universities and Leadership · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEliteExcellenceConsolidation (business)HomogeneousChinaLeagueAssociative propertyAssociation (psychology)Political scienceContext (archaeology)Public relationsPsychologyPoliticsBusinessGeographyLawMathematicsAccounting

Abstract

fetched live from OpenAlex

In the article, based on data from the Shanghai Ranking (ARWU) 2003-2022, ranking achievements of elite associations of top universities in the USA (AAU), the United Kingdom (Russell Group), Canada (U15 Group), Japan (RU11), China (C9 League), Australia (Group of Eight), Germany (U15), taking into account the political and economic context of their functioning, the factors of the effectiveness of such associations are determined to ensure the leadership of member universities. It has been found that the greatest synergistic effect of association is achieved in small associations or their parts (with the number of up to 10-20 institutions), which include institutions close in terms of ratings, which are characterized by significant (resonant) intragroup interaction and which are provided with strong national support. Activities at the same level of complexity of educational programs and research and development, selective significant cooperation on the principle of "equal to equal" (thus achieving the effect of resonant interaction) serve as a guarantee of successful collective advancement to the top levels of excellence of all participants of the interaction. Otherwise, the formal association either declines or undergoes informal internal stratification (differentiation) into groups more homogeneous in complexity of activity, not all of which can withstand the competitive struggle for leadership and the ability to fully use the advantages of formally open science due to its actual closedness in part complex knowledge for insufficiently capable universities. An alternative to the selective associative union of universities withing the country can be their direct organizational consolidation with systematic state support to transform into powerful university centers, as is done in France, or a strong national policy for the development of flagship universities, as in Switzerland. Scientifically based practical recommendations on the creation of a domestic elite Association of Ukrainian Universities (AUU) and the strategy of their consolidation are formulated in order to overcome the growing global backwardness of higher education of Ukraine, which is especially relevant in the conditions of the specifics of open science and the post-war recovery of the country on an innovative, high-intellectual, and high-tech basis.

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)0.000
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 score0.440
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.295
Teacher spread0.230 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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