CONTESTED GOVERNANCE AND LESSON DRAWING: A CRITICAL ASSESSMENT OF THE SOCIAL LICENSE MODEL IN CANADA’S AGRI-FOOD SECTOR (1998-2018)
Bibliographic record
Abstract
Focusing on Canada’s agri-food sector, this research offers a critical assessment of the social license model as a private self-governance regime. The study specifically seeks to understand the model’s role in the adoption and successful commercialization of biologically derived foods, products and innovation. \nOver the years, there have been several efforts to generate broad social acceptance of agricultural biotechnologies (agbiotech) because of the controversies they generate. Questions about acceptance and adoption of new technologies bring to the fore the concept of social license which has been adopted by many different sectors over the last two decades. Despite its widespread adoption, there remain significant gaps and variations in understanding, interpreting and implementing the model. One principal overarching question in this research is: if agbiotech acceptance is a major problem, is social license an effective solution? This study argues that social license will do one of two things in the pathway to agbiotech adoption: facilitate acceptance and successful commercialization or escalate rejection. \nThis study theoretically unpacks the governance of agbiotech using Ostrom’s Institutional Analysis and Development (IAD) Framework. Through this framework the research finds that social license is, in effect, an attempt to bridge two contrasting agbiotech regulatory architectures: the scientific rationality regulatory approach, premised upon the use of scientific evidence to assess potential risks of new biotechnologies, and the social rationality regulatory approach which supports a broader socio-economic mandate.\nThe qualitative empirical analysis for this dissertation draws on institutional and discourse analysis, three case studies of successful and unsuccessful agbiotech adoption and commercialization and 27 semi-structured interviews from various agri-food stakeholders across three Canadian provinces: Ontario, Alberta and Saskatchewan.\nThis research finds that the most noticeable achievement of the social license model is its ability to bring social considerations into mainstream corporate discourse and decision-making and puts the focus on the need for communication and education in the agri-food sector. However, social license as a governance model in Canada’s agri-food sector has been unworkable for several reasons. The concept is basically more rhetorical than methodical, with little or no evidence of an effective or successful implementation in this sector. This is further compounded by the variations and gaps in the understanding of social license among the diverse stakeholders, contributing to a general lack of consensus as to what social license means in Canada. There is also a misalignment between the original intent of social license and the principal challenges of Canada’s agri-food sector, with the result that social license is acting more as a veto than a bridge. Some stakeholders are already changing the narrative from social license to public trust.\nThe conceptual overlap and jurisdictional confusion between social license and corporate social responsibility makes social license more of a duplication of effort than a value addition and an incremental adjustment to other existing private self-governance regimes. Social license also fails to address the central issues in the overall governance and regulation of agbiotech: the minimization of risks and uncertainty. Elevating social license to a concept with veto power capable of supplanting government approval of agbiotech innovation is problematic for democracy, the rule of law and public policy and stifles innovation and development.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.053 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".