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Record W4388983263 · doi:10.12797/9788381388795.04

Powiat olkuski i nowe wyzwania po 24 lutego 2022 roku

2023· book-chapter· en· W4388983263 on OpenAlexaboutno aff
Monika Kwiatkowska, Dawid Berbeć

Bibliographic record

VenueKsiegarnia Akademicka Publishing eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEuropean Politics and Security
Canadian institutionsnot available
Fundersnot available
KeywordsLocal governmentLocal communityPublic administrationGovernment (linguistics)Political scienceFace (sociological concept)GeographySociologySocial scienceLaw

Abstract

fetched live from OpenAlex

In the face of the humanitarian crisis after 24 February 2022, Olkusz County faced new challenges. Local authorities took instant decisions to organize aid for Ukraine. From 28 February to 10 March 2022, over 10,000 people came to Olkusz. The establishing of the “county tent city” used as a reception point was the main initiative undertaken by the local government. Numerous county-level institutions and local non-governmental organizations were involved in the aid initiatives. They were coordinating the collection of essential goods, organized their transports to Ukraine and participated in the aid operations of communities and organizations from Germany, the UK, Canada, the US, and France. The contribution of volunteers who worked in the “county tent city” and collection points was undoubtfully crucial and invaluable. The local community showed empathy engaging in bottom-up initiatives and those coordinated by county-level institutions and local non-governmental organizations. The authors of this article analyzed primary sources, including data received from the local government of Olkusz County, and secondary sources in the field of history and characteristics of the region. They also made a description of the previously conducted in-depth interview with the county office representatives.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.008

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 head0.044
GPT teacher head0.270
Teacher spread0.226 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2023
Admission routes1
Has abstractyes

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Same venueKsiegarnia Akademicka Publishing eBooksSame topicEuropean Politics and SecurityFrench-language works237,207