Internally Displaced Persons in Canadian News Discourse
Bibliographic record
Abstract
Although the concept of environmental refugees has been circulating for more than thirty years, not much has been written about how the displacement of people caused by environmental disasters has entered into public discourses. More specifically, within the empirical investigations into the discourses on environmental displacement, the phenomenon of Internally Displaced Persons (IDPs) moving inside the borders of their own countries as an effect of disasters has received little critical attention with respect to how it is framed. The present article explores the discursive constructions of IDPs in Canadian news discourse which refer to the destructive 2016 Alberta fire in Fort McMurray, the heart of the tar sands region. Thus, a corpus of news reports is analysed in a discourse-analytical perspective with the intent of identifying specific discursive strategies, frames and patterns in the representation of the social actors within newspaper narratives. The analysis shows that the representations of IDPs in the corpus under investigation are characterized by different patterns of language choice compared to those emerging from the discourses on climate refugees, asylum seekers and migrants in general, evident in previous studies. In the end, what becomes also apparent is that nomination strategies are connected to specific ideologies in discourse, on the basis of which the correlation between the tar sands and the fire is either omitted or mildly unveiled.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".