'Social media in citizen-government relations around the world'
Bibliographic record
Abstract
E-Governance, once defined as an additional channel for service delivery and communication between public agencies and the citizens and businesses they serve, is quickly developing into a set of innovations in which web technologies converge with social media platforms and big data / artificial intelligence applications. Technologies are not necessarily neutral drivers of innovation, but rather these technologies are designed, constructed, implemented and used by people, and thus they shape and are shaped by the values, ideas and assumptions of policymakers, system developers, officials and citizens. In this lecture I would like to present some first results (‘impressions’) from the COSMICS (‘Comparative study of Social Media in Citizen-State Relations’) study I conducted together with Rebecca Moody. We gathered original survey data in eight countries (Canada, Paraguay, Algeria, Kenya, Netherlands, Greece, Pakistan & China) and tried to explain why citizens would use social media to report poor public sector performance (a form of ‘thin political participation’). First findings indicate that citizens’ use of social media to ‘speak up’ is associated with, in order of strength of effect, social influence / peer pressure (+), perceived effectiveness of social media use (+), trust in social media business infrastructure (+), social media ease of use (+), and citizens’ fear of consequences (-), with citizens’ trust in government not having an impact. It must be noted that impacts are different in various country subsets. One of the perhaps surprising outcomes of the study is that ‘trust’ plays an important role in enabling a vibrant digital democracy, yet it is trust in proprietary social media infrastructures rather than trust in government institutions that enables or limits citizens to engage in participatory practices. This finding urges us to rethink the longer term roles of proprietary social media platforms such as Facebook and Twitter (and arguably Weibo in the People’s Republic of China!) as infrastructures for citizen engagement and participation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".