Federal-Provincial Data Interoperability and AI Adoption: Leveraging the Current Federal-Provincial Dynamic and Canada-EU AI Strategic Partnership
Notice bibliographique
Résumé
Canada is at a critical juncture where major economic challenges and geopolitical tensions and the required transformation of public services require a new way of managing and operating federal-provincial-territorial (FPT) relationships. At the heart of this transformation is the imperative to establish public sector data interoperability. This approach not only enables more efficient service delivery and the adoption of AI but also contributes to national productivity and digital sovereignty. At the centre of Canadian public sector governance and operations is the citizen, who is also the taxpayer (individual or business), consumer and end user of services provided by various levels of government: federal, provincial, territorial, and municipal. Citizens are entitled to continuous, efficient, safe, and reliable public services, regardless of jurisdiction. Meeting this expectation is a shared responsibility that no level of government can assume alone. The response requires a collaborative, whole-of-governments approach based on modern tools and coordinated strategies. Despite efforts, Canada’s intergovernmental relations remain hampered by fragmented and often outdated systems and a lack of cohesion in governance structures, which together prevent the scalable and adaptable use of data across jurisdictions. In response to these structural challenges and an increasingly unstable international environment, the current federal government has identified a set of strategic priorities that are inherently dependent on high-quality, integrated data systems: adapting to climate change, preparing for and managing crises, improving domestic trade; addressing housing and affordability; protecting our sovereignty; modernizing the public service; and accelerating AI-enabled innovation. This report highlights that barriers to interoperability are primarily political and institutional rather than technical. Drawing on international models such as the European Union’s Interoperable Europe Act and lessons learned from policy frameworks put in place by the United Kingdom, the G7 and California, the report proposes a Canadian path rooted in federated governance, modular agreements and data architectures based on trust, respect for jurisdictional responsibilities and individual rights. The report recommends two urgent measures: concluding an FPT agreement on data interoperability and creating a permanent FPT board on AI and interoperability. These initiatives are not mere technical adjustments: they are strategic and fundamental to any nation-building project. They respond to the Prime Minister’s call to identify projects with lasting benefits. The proposed FPT interoperability framework agreement aligns directly with the national interest designation criteria set out in Part 2 of Bill C-5, the Building Canada Act. It strengthens Canada’s autonomy, resilience, and security by supporting the sharing of cyber-resilient infrastructure and the coordination of emergency response capabilities. It also offers clear economic and institutional benefits by improving service delivery, facilitating labour mobility, and fostering conditions conducive to productivity growth through AI. This is not a marginal reform, but a fundamental change: a transformation of how governments collaborate, guided by the principles of transparency, subsidiarity, and shared objectives.
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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 | 0,039 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,013 |
| Études des sciences et des technologies | 0,019 | 0,009 |
| Communication savante | 0,033 | 0,010 |
| Science ouverte | 0,005 | 0,018 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».