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Record W2954369596 · doi:10.17673/vsgtu-pps.2019.1.4

Results analysis of the regional research competition for secondary school students using information and communications technology

2019· article· en· W2954369596 on OpenAlexaff
Zulfiya Kamaldinova, Nadezda V. Kulikova

Bibliographic record

VenueVestnik of Samara State Technical University Psychological and Pedagogical Sciences · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSamaraCompetition (biology)Work (physics)Regional scienceState (computer science)Qualitative researchPublic relationsPolitical scienceMathematics educationSociologyPsychologyEngineeringComputer scienceSocial science

Abstract

fetched live from OpenAlex

The article deals with specific issues relating to the implementation of one of the priority directions of state policy of Russia is to work with gifted young people. The highly structured system of activities helped identify and develop gifted young people in science, engineering and technology and innovation development of the Samara region and allowed to define tasks and directions of the contests of students research projects in the competition "Vzlet".The article analyzes the results of the regional research competition held for the past 3 years in the Samara region for gifted secondary school students. The key quantitative indicators which may lead to further qualitative analysis were presented, specifically, the total number of participants, the number of submitted projects in different research areas, project advisors, participating educational organizations, and winning projects. In addition, the rates of those who started the competition and those who successfully completed it were analyzed. The articles offers conclusions and discusses future directions for the competition.

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 score0.918
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.352
GPT teacher head0.470
Teacher spread0.118 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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