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Record W7112418208

Frank the Animal

2025· article· en· W7112418208 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPinnacle
DOInot available

Abstract

fetched live from OpenAlex

Male stripper. Pimp. Drug dealer. Money collector. Professional hockey player. Frank the Animal is the story of a promising young hockey player’s meteoric rise from the depths of the Winnipeg underworld to the pinnacle of professional hockey. As the first-born son of poor Polish immigrants living in Winnipeg, Frank Bialowas rejected his parents’ conservative metric of success and dreamt of making something more of himself. Frank worked hard to become one of Manitoba’s toughest brawlers in junior hockey, but at eighteen-years-old, a local criminal lured Frank into Winnipeg’s seedy underworld because of its promise of fast money and beautiful women. Long before he was nicknamed “the Animal,” Frank Bialowas was better known in his native Winnipeg as Max the Millionaire, the stripper who pimped, sold drugs, and collected money for motorcycle gangs. After serving a stint in a Manitoba jail, Frank’s family convinced him to give hockey one last chance with a minor-pro team in Virginia. Frank reluctantly left behind his life of crime and went on to great success in the American Hockey League, where he became one of Philadelphia’s most iconic bad boys. Despite the success he enjoyed in professional hockey, Frank’s self-destructive tendencies resulted in many devastating setbacks throughout his playing career and personal life. Like other compelling antiheroes, Frank’s talent was limited only by his struggle to maintain selfcontrol.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0060.005
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.006

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDigital Access to Scholarship at Harvard (DASH) (Harvard University)Same topicCanadian Identity and HistoryFrench-language works237,207