Good code bad code: Exploring the immigration-nation dialectic through media coverage of the Herouxville 'Code of Life' document
Bibliographic record
Abstract
The media is widely held as a force that both shapes and reflects how citizens think about immigrants and immigration. This article explores two recent developments in the literature on media coverage of immigrants and immigration: the application of Hegelian dialectical theory to the study of discourses about immigration; and a debate concerning the shift from outright racism to subtler forms of ‘new racism’ and its implications for media coverage. The former development views the media as embodying oppositional constructions of ‘us’ and ‘them’, and argues that we stand to learn much about national self-conception by interrogating media narratives about immigration. The latter development suggests that while there have been progressive changes in the field of journalism, negative constructions of racialized immigrant Others persist in new forms. Here, we consider the intersection between these two developments. We adopt the dialectical approach to examine media coverage of the town of Hérouxville, Quebec’s 2007 publication of a ten-page warning about the ‘limits to accommodation’. Because it was written by ‘non-immigrants’ the publication of this document provides an ideal case with which to consider the ‘us’ side. We find that the media framed the document and its authors as racist and anti-immigrant, thereby inscribing a (problematic) definition of the ‘us’ side as being multicultural and anti-racist.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".