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Record W4245399985 · doi:10.32920/ryerson.14646147

Survival of the fittest: home exercise, moving image technology, and audiovisual preservation

2021· preprint· en· W4245399985 on OpenAlexaff
Olivia Babler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSurvival of the fittestVariety (cybernetics)HistoriographyAestheticsMultimediaVisual artsHistorySociologyArtLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This thesis explores the production, use and preservation of instructional fitness media made in the U.S. between the 1910s and 1980s. While film historiography has expanded to include many non-theatrical, ‘orphan’ works since the mid-1990s, home exercise—an enormously popular genre of the moving image—has received scant attention from media scholars and preservationists. This is primarily a material history of a genre that has been disseminated on a variety of home-viewing formats since the 1920s, many of which have become obsolete and may soon become inaccessible. Primarily drawing upon contemporary advertisements, reviews and trade publications, as well as conversations with archivists, producers, distributors and collectors, this study seeks to expose the forgotten early iterations of the home exercise genre while also exploring the less-discussed material aspects of the more famous recent examples.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0060.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.235
Teacher spread0.206 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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