CONTESTED GOVERNANCE AND LESSON DRAWING: A CRITICAL ASSESSMENT OF THE SOCIAL LICENSE MODEL IN CANADA’S AGRI-FOOD SECTOR (1998-2018)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,025 | 0,053 |
| Communication savante | 0,016 | 0,006 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».