Victims, burdens, and problems: A thematic analysis of Le Journal de Montréal news coverage of Roxham Road
Bibliographic record
Abstract
Roxham Road, a rural road in Quebec, was a longtime hotspot for migrants and asylum seekers entering Canada from the United States that the province's news media paid close attention to, until the entry point was closed in March 2023.Scholarly research has identified frames in the coverage of this controversial crossing point between 2017 and 2019, when the number of irregular arrivals was considered a "crisis."This article extends the research to the post-pandemic era, and during a new "crisis," by presenting an inductive thematic analysis of 55 online news articles published in Le Journal de Montréal, the province's most-read newspaper, throughout 2022.The results point to binary coverage that rarely speaks to migrants, and that portrays Roxham as a problem requiring policy-oriented solutions.Although aspects of the coverage remained stable in 2022, temporal and provincial specificities stand out, in particular the importance given to the French language, Quebec politics, and conflicts between the provincial and federal governments.Roxham Road, un chemin de campagne au Québec, a longtemps été un point névralgique pour les migrants et les demandeurs d'asile entrant au Canada depuis les États-Unis, un sujet largement couvert par les médias québécois, jusqu'à la fermeture de ce point d'entrée en mars 2023.Des recherches académiques ont identifié des cadres dans la couverture de ce point de passage controversé entre 2017 et 2019, lorsque le nombre d'arrivées irrégulières était qualifié de « crise ».Cet article prolonge ces recherches dans l'ère post-pandémique et lors d'une nouvelle « crise », en présentant une analyse thématique inductive de 55 articles de presse en ligne publiés dans Le Journal de Montréal, le journal le plus lu de la province, tout au long de l'année 2022.Les résultats révèlent une couverture binaire qui donne rarement la parole aux migrants et qui dépeint Roxham comme un problème nécessitant des solutions politiques.Bien que certains aspects de la couverture soient restés stables en 2022, des spécificités temporelles et provinciales se distinguent, notamment l'importance accordée à la langue française, la politique québécoise et aux conflits entre le gouvernement provincial et fédéral.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".