The Influence of Leaders on the Quality of Citizen Deliberation: An Exploratory Assessment of Online Deliberation in New Zealand
Bibliographic record
Abstract
Leaders can be essential in setting the tone of deliberation in the public sphere, but can their discursive style influence the wider public sphere? Mass communication usually mediates leader-citizen interactions, and the proliferation of social media has presented new, large-scale opportunities to support deliberation. Further, leaders using these platforms have widespread reach. Using deliberative discourse analysis, this exploratory research studies whether leaders influence the quality of citizen deliberation and whether this is dependent on the online arena. Two leaders with contrasting communication styles were chosen: New Zealand prime minister Jacinda Ardern and opposition leader Judith Collins. Two online arenas were included: a national news media Facebook page and the leaders’ respective Facebook pages. The results found that deliberative quality was variable within the news mass media arena; however, citizens displayed higher deliberative quality when the leader did so in the leaders-led online media arena. This study suggests that leaders can use deliberative dialogue to foster more deliberative discussion among citizens when they engage as both participants and facilitators in arenas with greater access to directly support deliberation. It presents theoretical arguments for leaders to participate in legitimation processes as part of the response to the problem of scale and introduces a communication model for leaders to support deliberation in the public sphere. The model suggests that the leader's ability to affect the deliberative quality of citizens' discussions is mediated by their level of influence within that space.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".