Is Social Licence A Licence To Stall?
Bibliographic record
Abstract
The School of Public Policy at the University of Calgary organized a one-day symposium on Oct. 8, 2014 in Calgary, as part of the School’s TransCanada Corporation Energy Policy and Regulatory Frameworks Program. The symposium was titled “Is Social License a License to Stall?” Held at the Hotel Arts, the event attracted a full-capacity audience of about 110 people, including representatives from industry, government and environmental non-government organizations. The symposium included four moderated panel sessions and a keynote speaker at lunch. The School of Public Policy set the framework for discussion at the Calgary symposium with the following description: Canada’s regulators act in the public interest to review energy and infrastructure project applications. Regulators are guided by procedural fairness and follow a transparent application, review and hearing process with data filings and sworn testimony. But that’s changing. “Social license” is a relatively new term, which some interests are using to create a different standard for the approval of projects — especially energy projects. According to social license advocates, projects must meet often ill-defined requirements set up by non-governmental organizations, local residents or other interests — a new hurdle for project approval, but without the rigour and rule of law of a regulator. Is social license a meaningful addition to the regulatory process, or is it being used as a constantly moving goal-post designed to slow down regulatory processes, delay project implementation, frustrate energy infrastructure expansion and even enrich those advocates who promote it as a new model? This paper summarises the discussion and the themes that emerged throughout the day. Most notably, panellists concluded that “social licence” is a real and significant issue that presents both an opportunity and a problem, not only for regulators but for all parties involved in the regulatory process.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".