Bibliographic record
Abstract
It has become axiomatic that a social license is a critical success factor for Canadian pipelines. Regulators may permit a pipeline, but on-the-ground consent for a project is a function of communities. Social license is an intangible quality outside of formal regulation, occupying the gap between community expectations and existing laws. Increasingly, gaining social license is seen as an important aspect of managing environmental and social risks, and the presence or absence of social license affects project budgets, timelines, corporate reputation and even project outcomes. There are regulatory risks to not demonstrating social license; and even with regulatory approval social license may be the difference between legal challenges and none. Social license is not easy to find, is difficult to measure, and is capricious and dynamic in nature. It is an inherently vague and changeable standard that means different things to different people. Simply defining social license can be a futile enterprise: as with US Supreme Court Justice Stewart’s famous 1964 judgment, we can’t neatly define social license, but we know it when we see it. The emergence of social media has meant that communities are better engaged, informed, and networked than ever before. Gaining social license happens when trust is built, earned and maintained with communities: it can take a long time to build that trust, and today’s digital citizen expects engagement across many platforms in order for that trust to be maintained. Though there is no ‘one-size-fits-all’ approach to gaining social license, the approach of this paper is to lay out a case-study roadmap for navigating towards it by building relationships, countering misinformation, and mobilizing existing support. The paper will also recognize potential wrong turns such as inattention to social media, lack of transparency or a clear message, and the mistaken belief that regulatory approval is the only approval necessary.
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 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.023 | 0.041 |
| Insufficient payload (model declined to judge) | 0.040 | 0.018 |
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".