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Record W3029213984 · doi:10.7202/1069008ar

In Search of George Scott

2020· article· en· W3029213984 on OpenAlexvenueaboutno aff
Robert Clarke

Bibliographic record

VenueOntario History · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)HollywoodShot (pellet)FilmmakingArt historyHistoryMotion pictureFilm industryMedia studiesEconomic historyMovie theaterSociology

Abstract

fetched live from OpenAlex

In the first decade of the twentieth century George Scott established a motion picture firm in Toronto. He filmed the “Great Toronto Fire” of 1904, attempted to show movies on Toronto Island, and took time out to open a “theatorium” in Peterborough, Ont., in 1907. Scott worked in the budding filmmaking industry in London, England, in Europe, and in Toronto, New York, and Hollywood. He also shot film around the world: from the South Pacific, Australia, Southeast Asia, and Japan to British East Africa. He died in Los Angeles in January 1929. Scott had a motion picture career of global proportions, but he is almost entirely known only for having produced one of the first films shot in Toronto. This article traces his life before and after his time in Toronto.

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)0.004
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: none
Teacher disagreement score0.568
Threshold uncertainty score0.859

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.005

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 head0.061
GPT teacher head0.206
Teacher spread0.145 · 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
Published2020
Admission routes2
Has abstractyes

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