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Record W4382245906 · doi:10.1515/9780773551831

Wrestling with Life

2017· book· en· W4382245906 on OpenAlexaboutno aff
George Reinitz, Richard King

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental science

Abstract

fetched live from OpenAlex

George Reinitz was twelve years old when he and his family were taken from Szikszó, Hungary, and deported to Auschwitz, where many of his family members were killed. As a boy on the brink of adolescence, he experienced the horrors of a Nazi death camp. Following his liberation he returned to his hometown where he remained for a few years before immigrating to Montreal in 1948 as part of the Canadian Jewish Congress’s War Orphans Project. In Wrestling with Life, George Reinitz recounts his vivid memories of childhood and his experiences in one of the worst places humans ever created. He recalls being tattooed with an unclean needle, eating raw potato skins to stave off hunger, watching his father get whipped in the face, and looking after the horses of SS officers. In Auschwitz he learned and used survival skills that he later applied in the commercial realm. George settled in Montreal and became a world-class wrestler, competing internationally and carrying the flag for the Canadian team at the 1957 Maccabiah Games in Israel. After working in a number of jobs he found his calling in the furniture business, eventually founding Jaymar Furniture, a leading manufacturer and a company that still operates successfully in Quebec. Wrestling with Life is a moving account of a child’s survival under the most difficult of circumstances. It tells the story of one man’s hard-won success as a businessman and athlete.

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)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.011
Scholarly communication0.0090.009
Open science0.0010.013
Research integrity0.0030.012
Insufficient payload (model declined to judge)0.0350.013

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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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