‘It's (not) who we are’: Representing the nation in US and Canadian newspaper articles about refugees entering the country
Bibliographic record
Abstract
Abstract Ideas about nation and national identity continue to be highly important, for both individuals and collectivities. In this article, I provide a cultural‐sociological reconstruction of the meanings of national identity conveyed in US and Canadian newspaper coverage of refugees entering, or potentially entering, the country. I engage Billig's theory of ‘banal nationalism’ and Anderson's idea of ‘imagined comEties’. In Canada, there is a single narrative that encapsulates ‘who we are’—a generous, welcoming country for people fleeing extraordinarily difficult circumstances, who will eventually integrate and succeed. National identity is processual, narrated as an ongoing ‘national project’. In the US, there are two distinct storylines about ‘who we are not’. Both begin with the country depicted as a humanitarian leader but diverge along political party lines. The Democrats quoted invoke history and ‘American values’ to say this is not a country that turns its back on those in need; the Republicans argue that this is not a country that exposes its people to harm, so refugee admissions must be halted. National identity is more solid, represented in passing; ‘who we are’ is taken for granted by spelling out ‘who we are not’.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".