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Record W1500425460 · doi:10.31165/nk.2014.64.322

The Irish in American Cinema 1910 – 1930: Recurring Narratives and Characters

2014· article· en· W1500425460 on OpenAlexfundno aff
Thomas James Scott

Bibliographic record

VenueNetworking Knowledge Journal of the MeCCSA Postgraduate Network · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
FundersMcGill University
KeywordsIrishMovie theaterNarrativeComedyHistoryCharacter (mathematics)Media studiesEthnic groupServantLiteratureGenealogyArtSociologyArt historyAnthropologyLinguisticsEngineering

Abstract

fetched live from OpenAlex

This paper will consider cinematic depictions of the Irish between 1910 and 1930. American cinema during these years, like those that preceded them, contained a range of stereotypical Irish characters. However, as cinema began to move away from short sketches and produce longer films, more complex plots and refined Irish characters began to appear. The onscreen Irish became vehicles for recurring themes, the majority of which had uplifting narratives. This paper will discuss common character types, such as the Irish cop and domestic servant, and subjects such as the migration narrative, the social reform narrative and the inter-ethnic comedy. It will also briefly consider how Irish depictions in the 1910s and 1920s compared to earlier representations. While the emphasis will be on films viewed at archives, including the University of California, Los Angeles Film and Television Archive, or acquired through private and commercial sellers, the paper will also reflect on some films that are currently considered lost.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.007
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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