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Record W4380481889 · doi:10.6000/1929-4409.2020.09.226

Scientific School Image Development of a University Based on the System of Public Relations

2022· article· en· W4380481889 on OpenAlexvenueno aff
Неонила Альфредовна Туранина, Olga Y. Murashko, Galina A. Kulyupina, Irina Fedoseevna Zamanova, Igor N. Perepelkin

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Competition (biology)InstitutionPublic relationsPopulationSociologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

The article considers the problem of educational institution promotion in the external socio-cultural environment via innovative management, in particular, the Public relations system. The solution to this problem is important in terms of fierce competition in the educational services market. The authors draw attention to the fact that scientific schools as unique associations of university science representatives can be considered as one of the effective elements of the university image policy development. They outlined the primary tasks of the university scientific school positive image development in the public relations system, PR tools are highlighted in the internal university environment and in the external sphere of the university, where the following segments are considered as the target audience: the scientific community outside the university, potential employers of university graduates, and the population represented by potential applicants and their parents.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.006
Scholarly communication0.0110.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.102
GPT teacher head0.298
Teacher spread0.196 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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