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Record W1987596115 · doi:10.5539/ies.v8n5p165

Scientific Degrees as a Status Characteristic of Russian University Teachers

2015· article· en· W1987596115 on OpenAlexvenueno aff
Irina Ilina, E.M. Kryukova, Alexandra Zotova, Maxim Georgiyevich Chardymskiy, Nelli Zinnurovna Skudareva

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryContext (archaeology)Modernization theoryPrincipal (computer security)Value (mathematics)SociologyHigher educationMathematics educationSocial sciencePedagogyPsychologyPolitical scienceMathematicsLawGeographyStatistics

Abstract

fetched live from OpenAlex

This paper gives an assessment of the social and motivational value of scientific degrees of Russian university teachers. The author analyzes the historical experience and current trends in the transformation of the system of degrees. The paper shows the dynamics and structural characteristics of teachers with different professional and qualification status in the context of modernization of higher education. An assessment of salary level according to scientific degree is given. Based on the results of a sociological survey, behavior strategies in respect of earning a degree of candidate and doctor of sciences are identified. The principal status advantages of degree holders are described. Reasons for the discredit of the Russian system of scientific degrees are identified.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.137
GPT teacher head0.387
Teacher spread0.250 · 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 designObservational
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

Citations10
Published2015
Admission routes1
Has abstractyes

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