MétaCan
Menu
Back to cohort
Record W4386934904 · doi:10.55908/sdgs.v11i7.1310

Peculiarities of Organization and Efficiency of the Educational Process Under Martial Law

2023· article· en· W4386934904 on OpenAlexaff
Олена Геннадіївна Білюк, Андрій Крап, Kostiantyn Herasymiuk, Kira Hnezdilova, Nadiia Kulchytska

Bibliographic record

VenueJournal of Law and Sustainable Development · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsUkrainianMartial lawProcess (computing)Subject (documents)Object (grammar)LawSociologyMartial artsPolitical scienceComputer scienceArtificial intelligenceLibrary scienceGeographyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Objective: The object of the research is the objective reality of analysis and assessment of the sphere of the educational process in conditions of uncertainty and loss of control over this process. The subject of the research is the specificity of the organization and the level of effectiveness of the educational process in the conditions of the martial law of Ukraine. Theoretical framework: Many scientific works have been devoted to the problem of organizing the educational process in conditions of force majeure and armed conflict in particular, as well as to the derivative issues (Caplan, 2018), (Jennings, 2001), (Pet’ko, 2012, 2014, 2017, 2020), (Kotiak, 2011), (Lysenko, 2009), (Mazur, 2010), (Miniailova, 2022), (Morze, 2010), (Olshanska, 2016), (Prybylova, 2013), (Riabchun, 2021), (Sviezhentsev, 2015), (Skrypnyk, 2005), (Shevchuk, 2022), (Semenets-Orlova, 2022), (Akimova et. al., 2022). A study was conducted based on Ukrainian universities about distance education during the conditions of the COVID-19 pandemic (Bakhov, I., Opolska, N., Bogus, M., Anishchenko, V. & Biryukova, Y., 2021), based on which it was proposed application of open and specialized geoinformation systems of education for students and postgraduates (Iatsyshyn, A., Iatsyshyn, A., Kovach, V., Zinovieva, I., Artemchuk, V., Popov, O., ... Turevych, A., 2020). Method: Researching the problems of the educational process under martial law is a rather specific activity and requires a specialized, non-traditional approach. This is due to numerous factors that are peculiar and relatively "new". Therefore, the methodological basis is the descriptive approach, which has the character of a description in revealing the regularities of the phenomenon, and the condition of the research is the laboratory approach. In general, the research is public and nationwide. And the main method is the method of non-formalized (traditional) analysis of documents directly related to education, and this is a qualitative method. Results and conclusion: In terms of the efficiency of school youth's acquisition of knowledge, the state of war undoubtedly significantly lowered the level. However, solely at the expense of valid teachers who remained in Ukraine and conducted classes in the region of unfavorable conditions and self-awareness of students in general, the effectiveness of teaching and the Ministry of Education and Culture only ensured the maintenance of such effectiveness. Implications of the research: The study's findings have several implications for the field of analysed issue. It emphasizes the importance of school youth's acquisition of knowledge, the state of war undoubtedly significantly lowered the level. This can help them develop the necessary skills and knowledge to handle various crisis situations effectively. Originality/value:This study contributes to the existing body of knowledge by specifically focusing on organization and efficiency of the educational process under martial law. It offers insights into the practical aspects of the issue and provides valuable perspectives from both future specialists and their educators.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Law and Sustainable DevelopmentSame topicEconomic Issues in UkraineFrench-language works237,207