The personalization of engagement: the symbolic construction of social media and grassroots mobilization in Canadian newspapers
Bibliographic record
Abstract
This article explores the symbolic construction of civic engagement mediated by social media in Canadian newspapers. The integration of social media in politics has created a discursive opening for reimagining engagement, partly as a result of enthusiastic accounts of the impact of digital technologies upon democracy. By means of a qualitative content analysis of Canadian newspaper articles between 2005 and 2014, we identify several discursive articulations of engagement: First, the articles offer the picture of a wide range of objects of engagement, suggesting a civic body actively involved in governance processes. Second, engagement appears to take place only reactively, after decisions are made. Finally, social media become the new social glue, bringing isolated individuals together and thus enabling them to pressure decision-making institutions. We argue that, collectively, these stories construct engagement as a deeply personal gesture that is nevertheless turned into a communal experience by the affordances of technology. The conclusion unpacks what we deem as the ambiguity at the heart of this discourse, considering its implications for democratic politics and suggesting avenues for the further monitoring of the technologically enabled personalization of engagement.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".