Public engagement in the <scp>W</scp>eb 2.0 era: Social collaborative technologies in a public sector context
Bibliographic record
Abstract
Abstract This article examines how social collaborative technologies have changed the nature and scope of e‐participation, showcasing several popular modes of engagement. It argues that the main implementation barriers to social media adoption are not technological, but rather organizational, cultural, and administrative. While there is enormous potential for W eb 2.0 and associated social media tools to expand public engagement, the design of such initiatives must recognize that in digital environments influence is earned through social reputation, not bureaucratic authority. Sommaire Cet article examine comment les technologies de collaboration sociale ont changé la nature et l'envergure de la participation en ligne, en mettant en valeur plusieurs modes populaires de mobilisation. Il fait valoir que les principaux obstacles à l'adoption des médias sociaux ne sont pas technologiques, mais plutôt organisationnels, culturels et administratifs. Alors que le W eb 2.0 et les outils de médias sociaux connexes présentent un énorme potentiel d'accroître la mobilisation du public, la conception de telles initiatives doit reconnaître que dans les environnements numériques, on acquiert de l'influence grâce à sa réputation sociale et non à son autorité bureaucratique.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".