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Record W4402235824 · doi:10.47745/ausfm-2024-0006

The National Film Board of Canada and the Canadian Discourses of Immigration

2024· article· en· W4402235824 on OpenAlexaboutno aff
M. Ayaz Naseem, Adeela Arshad‐Ayaz, Hedia Hizaoui, Liam McMahon, Muhammad Akram

Bibliographic record

VenueActa Universitatis Sapientiae Film and Media Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationPolitical scienceMedia studiesSociologyLaw

Abstract

fetched live from OpenAlex

In this paper, we examine the articulation of immigration discourse in the National Film Board of Canada (NFB) film productions. We also address the interdiscursivity of “racialized discourse” and “economic discourse” regarding immigration, as articulated in these films. Specifically, we use insights from Fairclough’s Critical Discourse Analysis to examine how documentary films by the National Film Board of Canada both construct and hide Canadian exceptionalism. We argue that exceptionalism constituted in NFB media discourse creates an “imaginary” of immigration as an altruistic and ethical practice. At the same time these discourses obscure the fact that Canada’s immigration discourse is largely driven by economic motivations. White Canadians are portrayed as good global citizens with virtues such as tolerance, neutrality, openness, inclusiveness, fairness, social justice, etc. On the other hand, only those immigrants who are willing to assimilate/integrate into the Canadian imaginary are included in the imaginary. We take a sample of three documentary films produced by NFB from 1949 to 1998 to have a longitudinal look at the propagation and perpetuation of exceptionalist discourses on immigration and to argue that notwithstanding the benevolence inherent in policy and academic discourses the prime motivation behind acceptance of immigrants has always been economic.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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.

Study designNot applicable
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 routes1
Has abstractyes

Explore more

Same venueActa Universitatis Sapientiae Film and Media StudiesSame topicCanadian Identity and HistoryFrench-language works237,207