Bibliographic record
Abstract
Activist groups and others opposed to drilling operations have used social media with great effect to influence community perceptions of CSG and other mining operations. When faced with the reality of the level of community outrage apparent in social media, many industry executives throw their hands up in despair and conclude they cannot influence the discussion. Others decide that the best approach is to run broad-based advertising and PR campaigns that present only the industry’s case — after all, this approach worked well for the mining industry in opposing the mining tax. Neither of these approaches actually addresses or mitigates the outrage that exists in the community or provides a hope of resolving the underlying issues. This extended abstract presents a staged approach to social media engagement using land access as an exemplar, which builds on more than a decade of risk communications experience and applies this to creating engagement and influence in the social media sphere. The approach has five stages. These stages involve: Listening — to key issues and influencers;Understanding — the expectations and outrage factors that emerge;Following — tracking how the conversations are evolving;Engaging — starting to participate in the conversations and only then;Influencing — having built a presence and a community you may start to shape the conversations. You may not always like what you read and hear in social media, but if you participate in a considered way you will get an accurate picture of community expectations and earn the right to help shape the conversations.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".