Great Expectations: Perceptions and Policy Implications of the Social License Within Saskatchewan’s Agricultural Sector
Bibliographic record
Abstract
The ‘social license to operate’ (SLO) is a concept originating in the mining industry which describes a project’s ability to earn and maintain trust or approval of public stakeholders. A recent controversy surrounding the decision of Earls Restaurants to switch from Canadian suppliers of beef to an American provider has introduced SLO into the agricultural sector. Recently, the Saskatchewan Ministry of Agriculture explored a social license framework, representing the SLO’s transition from the private to public sector. This thesis investigates how the SLO has redefined itself to meet the expectations of increasingly mobilized consumers, and its potential effectiveness as a policy instrument. \nThe findings of a case study analysis and expert interviews reveal that the most recent iteration of the Ministry SLO framework is founded on the ‘knowledge-deficit’ approach to scientific communication, where the role of the public is restricted to passive recipient of information in the consultation process. The conclusion is that the reinforcement of this mindset is unlikely to be effective in fulfilling public needs around engagement on contentious agricultural topics. Policy-makers interested in the SLO approach must ensure that the process is adapted to address industry specific concerns in a manner that is truly responsive and accountable. This research has implications for how policy-makers engage stakeholders and communicate risks in a manner that maintains public trust and legitimacy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".