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Analysis of an Academic Environment as a Place of Studies and Work

2014· article· en· W118990414 on OpenAlexaboutno aff
Anastasiya Sizykh

Bibliographic record

VenueVoprosy Obrazovaniya/ Educational Studies Moscow · 2014
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityAppealWork (physics)Order (exchange)Higher educationSociologyPublic relationsPsychologyPolitical sciencePedagogySocial psychologyEngineeringEconomicsLaw

Abstract

fetched live from OpenAlex

In order to find out the causes of universities’ waning popularity as a place of work, the researcher sets the following tasks: to analyze specific features of an academic career to determine at which stage it starts to lose its appeal; to reveal influences in choice of postgraduate courses as an alternative to introduction to the labour market, as well as to reveal influences in choice of a university as a place of work; to evaluate advantages and disadvantages of taking postgraduate courses and working at a university. The researcher conducted 27 semi-structured inter views with the facult y and postgraduates in th ree Russian higher education institutions and one Canadian one: the National Research University “Higher School of Economics”, Moscow State University of Economics, Statistics and Informatics, Siberian Federal University (Krasnoyarsk) and University of Winnipeg (Winnipeg, Canada). On the basis of the findings maps of career lines are produced as patterns of changes in terms of positions (job positions or geographical ones) during a person’s job cycle; the research also revealed types of careers followers of which are of potential interest for Russian universities in terms of professional personnel reinforcement. The researcher found that motivation to take postgraduate courses is not limited by an interest to scientific work but is mostly connected to alternative objectives (like achieving a certain status, draft evasion) or to absence of any definite goal (reluctance to leave the university because of an attachment to it, a sense of discomfort because of the necessity to search for a job outside it). It is shown in the article that on the whole the academic career structure in Canada is similar to that in Russia, but perception of advantages and disadvantages of a work in an academic setting is different for each country.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.051
GPT teacher head0.353
Teacher spread0.302 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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