Social media, migration and the platformization of moral panic: Evidence from Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a contentious issue affecting the character, boundaries and future of social order, migration represents a recurrent source of moral panic. While analysts have considered conventional outlets’ role in triggering collective alarm, less is known about social media’s effects on migration’s construction as a social problem. Working with an original dataset of tweets from the 2019 Canadian election, a period of heightened concern and outcry for significant portions of the electorate, this paper employs content analytic methods to assess migration’s online demonization and interrogate the patterns of framing, participation and engagement brought within the issue’s orbit. Alongside documenting significant disquiet and antipathy, its findings suggest that Twitter is transforming panic production and facilitating forms of reaction involving mass-participation and collaboration; interference from automated ‘bots’ and considerable dispute, dissent and negotiation. Based on these results, the sensitizing concept of platformed panics is proposed to capture how social media’s technical affordances, design and appropriation align to promote moral panics that are networked, algorithmic and contested.
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.
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 | 0.004 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it