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Record W4250802280 · doi:10.32920/ryerson.14647950

Frame by frame: a comparative analysis of professional and amateur films of the 1939 Royal Tour of Canada

2021· preprint· en· W4250802280 on OpenAlexaffabout
Eveline Todd

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsToronto Metropolitan UniversityRoyal Ottawa Mental Health CentreQueen's University
Fundersnot available
KeywordsAmateurShot (pellet)PoliticsGeorge (robot)Variety (cybernetics)Order (exchange)Frame (networking)Media studiesCraftHistoryAdvertisingVisual artsPolitical scienceArtArt historySociologyEngineeringLawBusinessArchaeologyTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

This thesis examines ten professional films and ten amateur films depicting the royal tour of Canada, the United States, and Newfoundland taken by King George VI and Queen Elizabeth in the spring of 1939. The films, held in the collections of Library and Archives Canada, come from a variety of primarily Canadian producers including provincial and federal governments, newsreel companies, local production companies, and amateurs. A shot-by-shot analysis of each film is used to quantitatively and qualitatively investigate and compare the content, cinematography, and editing styles chosen by these filmmakers, in order to understand the differences and similarities in how they experienced and recorded the tour, and what these differences and similarities reveal about the tour’s impact on Canada and Canadians. This analysis finds that the amateur films tend to show how the tour affected individuals and communities, while the professional films trace its impact on a national, political level.

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)0.013
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 score0.197
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.011
Science and technology studies0.0140.006
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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
Published2021
Admission routes2
Has abstractyes

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