Fostering cooperative community behavior with IT tools: the influence of a designed deliberative space on efforts to address collective challenges
Bibliographic record
Abstract
How to encourage cooperative behavior and facilitate collaboration amongst diverse stakeholders to achieve collective goals remains a longstanding question in realizing a community’s capacity for local problem solving. Governments have increasingly adopted inclusive processes to engage non-state actors, and especially active engagement of citizens and communities in solving local policy challenges. Yet, the success of this inclusive approach depends on whether and to what extent all involved individuals, interest groups, communities, and government agencies can collectively deliberate and work together. We conducted experiments to explore the potential of IT-facilitated communication environment designed for deliberation activities to address collective challenges. Our unique experimental site for this research is a designed deliberation space that can seat up to 30 participants surrounded by the 260-degree seven-screen communal display. Our study shows that when people deliberate on a local community challenge under the environment with a communal display, they show more cooperative behavior in a social dilemma scenario than those who deliberate on the same challenge presented on individual displays. This study highlights the potential of technology’s influence on public deliberation in such a way as to promoting collective behavior.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".