Digital media and international migration: Twitter discourse during the Canadian federal election
Bibliographic record
Abstract
Abstract As a contentious, multidimensional issue, migration attracts significant media attention in affluent societies. While analysts have assessed coverage in traditional outlets, less is known about social media – digital platforms that facilitate the creation and sharing of content online. Working with a unique dataset of tweets from the 2019 Canadian federal election, this study analyses migration's representation within visible digital spaces. Employing content analytic methods, it offers new insight into the patterns of participation, claims‐making and engagement associated with the topic's online depiction. Alongside documenting significant lay involvement and creativity, it reveals communications were slightly negative and, reflecting the contemporary political climate, significantly more likely to feature identity‐based issues than economic and redistributive concerns. Messages from professional broadcasters, as well as, those featuring negative sentiments and referencing cultural matters generated greater engagement. The implications of these results and recommendations for future research are considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".