Doubtful dialogue: how youth navigate the draw (and drawbacks) of online political dialogue
Bibliographic record
Abstract
Social media platforms like Twitter are venues for 24/7 political discussion – including deliberation, everyday banter, and bickering. For youth, these platforms offer new opportunities and risks for participation, and suggest corresponding implications for civic education. This qualitative, exploratory study examines how 15 civic youth (ages 15–25) in the United States define and carry out political dialogue on social media platforms. We compare youths’ reported online dialogue strategies with strategies observed in digital artifacts of their posts. Findings suggest that youths’ conceptions of good online dialogue and its key ingredients – knowledge, respect, and diversity – are aligned with their practices in many respects. However, juxtaposing artifacts of youths’ online dialogue threads with reported strategies surfaced disjunctions, related to (1) perceived dialogue style and (2) perceptions of the value of online dialogue. Building on recent studies of novel classroom approaches, this study suggests promising entry points for educators and curricula to support youth to navigate the risks and opportunities of online spaces for civic expression and dialogue.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".