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Record W4401407045 · doi:10.1111/caje.12730

Political backlash and consumer boycotts: Evidence from the NFB relocation and movie demand in Canada

2024· article· en· W4401407045 on OpenAlexfundvenueaboutno aff
Ricard Gil, Jingyi Xing

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsRelocationPoliticsBoycottPublic opinionPolitical scienceAdvertisingAmericanizationAttendanceOpinion pollGovernment (linguistics)BacklashPublic administrationBusinessLawEngineering

Abstract

fetched live from OpenAlex

Abstract In this paper, we investigate the impact of the announcement in 1952 of a change in Canadian cultural policy, namely the reorganization of the National Film Board (NFB) and the move of its headquarters from Ottawa to Montréal, on movie demand. Using weekly box office revenue data for a subsample of movie theatres in Toronto and Montréal from 1945 to 1955, we estimate the impact of this policy change with a triple difference estimator and find that the NFB headquarters move in Canada was followed by a decrease in movie attendance for movies produced in anglophone countries and an increase in movie attendance for French movies in Montréal. We complement our analysis with Odesi public poll Canadian data from 1949 to 1959 and find that poll respondents from Quebec held a more negative opinion about the decisions of the Canadian government and the tide of Americanization, relative to respondents elsewhere, and that their opinion deteriorated further after the relocation announcement and the relocation itself took place. This finding is consistent with our hypothesis that the relocation of NFB headquarters caused political backlash and triggered a boycott against anglophone, especially American, movies in Quebec.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.142
GPT teacher head0.230
Teacher spread0.088 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicMedia Influence and PoliticsFrench-language works237,207