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Record W6939845703 · doi:10.6093/2035-8504/8580

Internally Displaced Persons in Canadian News Discourse

2021· article· en· W6939845703 on OpenAlexaboutno aff

Bibliographic record

VenueUniversità degli Studi di Napoli Federico II · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsCritical discourse analysisRefugeeNewspaperIdeologyRepresentation (politics)Perspective (graphical)Discourse analysisDisplaced person

Abstract

fetched live from OpenAlex

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.058
GPT teacher head0.309
Teacher spread0.251 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueUniversità degli Studi di Napoli Federico IISame topicClimate Change, Adaptation, MigrationFrench-language works237,207