Bibliographic record
Abstract
<JATS1:p>This book offers scholars and industry practitioners in the arenas of policy analysis, politics and media communications a method for astutely guiding large-scale policy and projects through the complex and changing landscape of a 24/7 news media. It is underpinned by empirical research that identifies and endeavors to close a considerable gap in current understanding and practice. This gap represents a failure to recognise and respect many powerful influences and associations that surround a policy arena that has drawn the ire of the news media. The result of this failure is ineffective communication that does little to advance the policy piece and, in the worst instances, leads to policy immobilization or poor policy decision-making.</JATS1:p> <JATS1:p>The author’s research spans a decade and two cities - Sydney, Australia and Montreal, Canada. The focus is on three metro-style rail infrastructure case study projects. One project is ongoing; one failed; and one is being upgraded, having recently reached fifty years of age. Through media, expert and public research this book builds an irrefutable case that the news media is highly influential to policy – and that these influences are complex, messy and changing. Drawing significantly on Actor-Network Theory, Richardson identifies the influential actors and alliances at play when policy is subjected to media discourse, and he proposes a framework for tracing and managing them. In doing so, he demonstrates that such a framework is not only vital for the successful negotiation of policy and projects in the media but also to an (r)evolutionary recasting of public, expert and media actors in the development and decision-making process.</JATS1:p>
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.
How this classification was reachedexpand
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".