Bibliographic record
Abstract
Introduction Recognising the importance of the diffusion of ideas and learning to policy change, policymakers have utilised social tagging to pressure governments to propose new legislation or forestall existing bills (Ems, 2014; Jeffares, 2014; Saxton et al., 2015). Among the many examples seen in the last decade include the use of Twitter by the Chicago Health Departments to discourage electronic cigarettes (Harris et al., 2014), by politicians to frame healthcare (e.g. #Obamacare) (Hemphill, Culotta and Heston, 2013) or to protest a lack of action on climate change (Segerberg and Bennett, 2011). This chapter will focus on the connection between social tagging as it is understood in the online environment and its connection to the apparatus of the state. Research on the use of social tags for identifying important legislation, promoting scientific knowledge or consulting the public is a growing yet uncertain area of study (Harris et al., 2014; Jeffares, 2014; Kapp, Hensel and Schnoring, 2015; Shapiro and Hemphill, 2014, 2017). Still, the potential of the internet to help bridge the gap between citizens and the state continues to be both an aspiration and a disappointment for the field of internet governance. Social tagging in information science refers to ‘the practice of publicly labeling or categorising resources in a shared, on-line environment’ (Trant, 2009, 1). For sociologists, the novelty of social tagging lies in its public nature compared to more private forms of coding in sociological field work (see Postill and Pink, 2012), but for political scientists the public naming of resources is not new at all. Removing the term online from this definition does not erase a wide array of social organization that occurs on a daily basis. The rather arbitrary naming of items in the budgeting process of a government (e.g. for clean technology), in particular, involves public labelling and categorising of resources, conducted by members of affected organizations, political leaders, policy-makers and, in some cases, individuals with unique interest or power in the particular policy area. Social tagging itself is not a new process and is closely connected to the role of institutions in public life. In policy theory, institutions refer to systems of formal and informal rules and routines in a society that have accumulated over time.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".