Bibliographic record
Abstract
Given its significance for society’s character, future and identity, migration has dominated media discourse. At present, the ascendance of digital platforms which broaden opportunities to produce, share and access content online has ignited debates about migration’s discursive construction. Often approached as promoting tolerance and inclusivity, social media are also believed to unleash xenophobia and intergroup antagonism. Working with a cross-section of tweets from the 2019 Canadian Federal election, this article asks how was migration framed, which users influenced the flow and substance of discourse and did Twitter diverge from conventional media space? It finds, while chains of citizen-users overwhelmingly employed Twitter to distribute original content, anti-immigration communications and actors were disproportionately featured. Considering these results, this article introduces the concept of digital nativism to clarify how technical affordances, user intentions and wider socio-political conditions intersect to produce emergent patterns of anti-immigration discourse and mobilization that are participatory, interactive and broadly distributed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".