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Общественная солидарность в период пандемии: международный опыт

2022· article· en· W4311458226 on OpenAlexaboutno aff
Olga P. Noskova

Bibliographic record

VenueSocialʹnye i gumanitarnye znania · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSociopolitical Dynamics in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityPandemicPolitical sciencePopulationIsolation (microbiology)Civil societyEconomic growthPublic relationsCoronavirus disease 2019 (COVID-19)SociologyPolitical economyPoliticsLawMedicineEconomics

Abstract

fetched live from OpenAlex

The article provides a meaningful analysis of domestic and foreign websites, which reflects up-to-date information about the peculiarities of civil activism and public solidarity during the COVID-19 pandemic in such countries as: Great Britain, Canada, France, Australia, America, Mexico, Russia. In the course of the analysis, similar assistance programs, common motives of people to engage in volunteer activities are highlighted, and unique projects related to both the geographical location of regions and the mentality of citizens are indicated. An attempt has been made to prove that volunteering during the pandemic is an effective tool for the formation of social solidarity around the world, and its manifestation in difficult times is a necessary and effective way to preserve the social health of the population and maintain the socio-economic balance of states. It is concluded that self-isolation served as an impetus to strengthen social ties between people and led to the emergence of new forms of interaction, as well as support options for various categories of citizens. In conclusion, the statement is made about the importance of cooperation between civil activists and regional governments in terms of organizing productive interaction and providing prompt mutual assistance in the emerging challenges of unstable and rapidly changing reality.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.021
GPT teacher head0.330
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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