The National Film Board of Canada and the Canadian Discourses of Immigration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".