MétaCan
Menu
Back to cohort

Polish Veterans of the Polish Armed Forces in the West: Who Decided to Emigrate to the USA and Canada after World War II – an Oral History Perspective

2019· article· en· W2981394737 on OpenAlexaboutno aff
Kamil Kartasiński

Bibliographic record

VenuePrace Historyczne · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationPerspective (graphical)World War IIOral historyFirst world warEconomic historyHistoryPolitical scienceAncient historyLawArchaeologyArt

Abstract

fetched live from OpenAlex

The aim of this paper is to characterize the generation of Polish veterans of the Polish Armed Forces in the West, who emigrated to the USA and Canada after 1945 in an oral history perspective.The author, using interviews conducted with the veterans, tries to show the motives of emigration to the USA and Canada, the fi rst years of veterans' life in exile and refl ection that accompanies veterans at the present time.The author of the article tries to recognize and capture the meanings contained in the veterans' emigre stories,using the oral history workshop on the basis of the analyzed sources. Keywords: Oral history,

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.008
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePrace HistoryczneSame topicOral History, Memory, Narrative AnalysisFrench-language works237,207