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Record W2115726182 · doi:10.5539/ies.v8n5p179

The Development of Technology for Higher Education Institution’s Administrative Personnel Assessment

2015· article· en· W2115726182 on OpenAlexvenueno aff
Olga Yurevna Bakhtina, Andrey V. Kirillov, Sergey Askoldovich Matyash, Olga Aleksandrovna Urzha

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionHigher educationOrder (exchange)Scale (ratio)Process (computing)PaymentBusinessRating scalePublic relationsPsychologyEconomicsSociologyPolitical scienceComputer scienceEconomic growthFinance

Abstract

fetched live from OpenAlex

The main approaches for development of assessment technology of higher education institution’s administrative personnel are considered in this article. Alongside with it the following is defined: purposes, formation principles, indicators and criteria of the assessment system, rating scale, the procedure and order of assessment process of higher education institution’s administrative personnel personal performance. Criteria of an assessment of personal efficiency of employees of higher education institution are considered in case the employee significantly exceeds expectations (“image” of a perfect employee), exceeds expectations, meets expectations, needs an improvement and doesn’t meet expectations (“image” of the bad employee). The procedure of payments of the individual size of an award of the worker proceeding from the received assessment is shown.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.088
GPT teacher head0.380
Teacher spread0.292 · 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
GenreMethods

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

Citations11
Published2015
Admission routes1
Has abstractyes

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