Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it