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Record W4293015492 · doi:10.16926/sit.2022.02.01

Achievements of Poland’s national team in the European Women’s Basketball Championships in the years 1938–2021

2022· article· en· W4293015492 on OpenAlexaboutno aff
Michał Skalik

Bibliographic record

VenuePrace Naukowe Akademii im Jana Długosza w Częstochowie Kultura Fizyczna · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballChampionshipMedalPolitical scienceGold medalWorld War IIEconomic historyHistoryLawArt historyArchaeology

Abstract

fetched live from OpenAlex

The beginning of basketball in the world dates back to 1891 when a Canadian, James Naismith, invented the game for students in Springfield. After a short time, matches were played in Europe, in Paris (1893) and London (1894). The first demonstrational game in Poland was played by women in 1909, in Lviv. The discipline spread throughout Europe after World War I. In the 1920s, some state and international organizations were established to standardize the rules of the game. They allowed to play the first national championships and afterwards to organize interstate matches. In 1935, the First European Men’s Basketball Championship was organized, and three years later, women made their debut in the competition of this rank. Between 1938 and 2021, there were thirty-eight editions of the championships, in which the Polish national team participated twenty-nine times. Most medals were won by athletes from the Union of Soviet Socialist Republics (USSR), Czechoslovakia, France, Bulgaria, and Spain. Poland’s most outstanding achievement was the gold medal won in Katowice in 1999. What is more, Polish women won two silver medals (1980,1981) and two bronze medals (1938,1968).

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.004
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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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