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Record W4388601185 · doi:10.55908/sdgs.v11i11.1670

Education Policy of 3RD Generation Universities

2023· article· en· W4388601185 on OpenAlexaff
Sarkhan Jafarov

Bibliographic record

VenueJournal of Law and Sustainable Development · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsCanadian Association of Nurses in Oncology
Fundersnot available
KeywordsPolitical scienceReputationHigher educationVocational educationService (business)Public relationsState (computer science)Economic growthPublic administrationBusinessMarketingEconomics

Abstract

fetched live from OpenAlex

Background: One of the tasks of the education policy of any state is the spread of its influence in the international arena. Today, universities are striving to take and strengthen their positions in the world ranking systems, demonstrating the status and reputation of universities in the global market of educational services. This ranking serves as a guideline for future students and their parents when choosing a place to receive a prestigious education and in the formation of individual trajectories of vocational training. Approach: There are several approaches in the world to determining the criteria for assessing a state's education policy. But despite the differences, they all document the growing role of modern universities in the development of society. In many respects, it is important both in terms of guaranteeing the sustainability of society and in terms of ensuring a breakthrough direction in its development. In contrast to recent times, this role has grown significantly. Due to the fairly conservative and limited social system in its function, such universities become a central link in the development of innovative economies and social spheres - the science that produces the socio-economic development of a particular region, Education, innovation centers, national or as well as global processes. Results: It covers all areas of the university, from cooperation with students to financial and scientific activities. A prime example of such an association is the International Strategy Advisory Service (ISAS 2.0) of the International Association of Universities or Universities (IAU). Conclusions: Around the world, states are trying to stimulate and support these processes. In many countries, this is facilitated by opposition from high-tech companies to the reduction of the final amount of independent basic and applied research, in favor of collaborating with universities on basic research projects. As part of its innovative activities, University 3.0 is involved in supporting business activities, analysis and consultation by experts from local governments and local authorities, opening facilities, and infrastructure for citizens, monitoring regional development, services to citizens are provided for lifelong learning and support student entrepreneurship projects that take into account the interests of the community.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0110.004
Open science0.0010.006
Research integrity0.0110.005
Insufficient payload (model declined to judge)0.0150.001

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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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