Using regional community consultative committees to improve stakeholder collaboration
Bibliographic record
Abstract
The importance of stakeholder engagement is often discussed, but there is something more powerful: stakeholder collaboration. It is one thing to have your stakeholders fully informed and understanding of proposals, but how much more beneficial would it be if they partake in creating proposals? Success among all parties is a value shared by staff on the $18.5b Santos GLNG CSG to LNG Project. While regular and meaningful engagement has been critical to the active stakeholders (more than 4,000) impacted by the project, the greatest community outcomes have occurred through collaboration. The concept of collaboration applies just as much to the internal stakeholders of an organisation as it does to the external audience. This extended abstract focuses on collaboration opportunities with external stakeholders using examples from the Santos GLNG Project. ‘The whole is greater than the sum of its parts’, an often-used quote from Aristotle, is fundamental to the notion of stakeholder collaboration. Far greater results can be achieved by working together. In 2009, Santos created the Roma Community Consultative Committees (RCCCs) collaboration. Such was the success of this committee that it has since been mandated by the Queensland Coordinator-General as a standard condition of approval of new major projects. Santos has also expanded the concept of the committee to include industry partners. These committees now operate in every regional council area impacted by the project. RCCCs bring together key community leaders every quarter to discuss social issues, primarily generated either directly or indirectly from the activities of the Santos GLNG Project.
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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 | 0.139 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".