It’s complicated : exploring Facebook’s potential for deliberative public engagement on sustainability policy
Bibliographic record
Abstract
The explosion of low-cost interactive digital tools like Facebook has prompted governments around the world to experiment with online public engagement. However, the media’s rapidly changing nature, combined with limited research in the field, means that little is known about how social media impacts who participates, how they participate and what is contributed in these online public engagement activities. To address these questions, the researcher convened over 500 people in a Facebook-based deliberation about transportation policy for the City of Vancouver, Canada, which resulted in recommendations that were considered by the City in drafting a long-range plan. Two hypotheses are explored: (1) locating a deliberative public engagement on Facebook can address some key challenges of in-person deliberation, and (2) deliberative discourse can be cultivated within a social media environment. An analysis of Facebook’s affordances – access to publics, distributed time and space, collaboration and learning, cross-platform connectivity, and social character – suggests that its properties both improve and impede the deliberative qualities of public engagement such as demographic representativeness, frequency of participation and reflective opinion sharing. Measuring participant discourse according to criteria of deliberativeness – discussion coherence, disagreement, opinion justification, engagement, and equality – demonstrates that this case study fostered the individual role in deliberation such as topical coherence, justified opinions and equal access to the discussion. However, the e-deliberation was not as successful as hoped in eliciting group aspects of deliberation such as disagreement and engagement with one another’s contributions. Together, these investigations provide a cautious but optimistic view on social media for deliberative public engagement. The study concludes by pointing to the need for further attention in social media based deliberation to engagement process design, participant recruitment and discussion tool development.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".