Reflections on the Value of Systems Models for Regulation of Medical Research and Product Development
Notice bibliographique
Résumé
INTRODUCTION (1) In a recent editorial in Science, (2) Bill Wulf used a framework to construct a model for in the life sciences. He defined an innovation ecology as the various interrelated institutions, laws, regulations and necessary to underwrite successful commercialization of publicly funded research through an infrastructure that entails education, research, tax policy, and intellectual property protection, among others. (3) In this formulation, private intellectual property and regulatory (IPR) rights form the linchpin between innovative publicly funded medical research, reduction to practice of basic research by firms and university technology transfer offices, product approval and marketing by government and firms as well as public consumption of approved medical products. As such, 'large scale' IPR rights-intensive translational research and technology commercialization constitute important market push and pull levers for domestic governments and provide the legal and regulatory basis for the drug development cycle writ large. Even so, and as lamented by Wulf in his editorial, a narrow size fits all IPR rights framework has the potential to stifle rather than encourage innovation. Casting the landscape as an open complex organic rather than a closed historical linear model of basic-to-applied research (4) is consistent with newer open-ended analytical models such as complex adaptive systems, (5) network dynamics (6) and dynamics. (7) These 'systems' frameworks view and model as dynamic, adaptive and indeterminate networks where the behavior of the system as a whole is governed by the ever-changing and non-linear nature of the connections between actors and institutions rather than as a predictable sum of a set of linear deterministic nodes. At the heart of the functioning of a complex adaptive system is the number and nature of the interactions between network nodes, which produce novel and ever changing properties as the layers of complexity increase. This dynamic structure-function relationship of complex is nicely summed up by the phrase more is different. (8) One implication of a view of IPR rights-intensive in the medical and life sciences is that local ecologies are collapsing globally. (9) This is due, among other things, to the global reach of patent decisions of first instance such as that in KSR International Co. v. Teleflex Inc., (10) harmonization of regulatory processes and standards, such as those relating to biomedical product approval, marketing and patenting, adoption of international IPR rights-sensitive instruments such as the WTO's Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) and, less obvious, the convergence of national science and technology (S&T) policies and normative behaviors aimed at commercialization of publicly funded medical research. Within the larger political and legal cultures of participating nations, there is an increasing space being carved out for translational research and commercialization. Indeed many nations, including Canada, are in the process of implementing strong IPR rights regimes that explicitly encompass publicly funded research efforts in order to reproduce the phenomenal success of university technology transfer and commercialization in the United States. This effort is hardly unique to Canada. Not only are other jurisdictions attempting to emulate U.S. translational research, but the United States itself, self-reflective after 25 years of Bayh-Dole, (11) is seeking to identify new and improved ways of commercializing public research in the context of its public health mandate. In the context of this debate, one hears increasingly vocal deliberation over the value of closed IPR rights models. PURPOSIVE POLICY Despite the growing visibility of network (12) and other systems theories, (13) linear models of organizations and organizational change have and continue to dominate analyses of the behavior of individuals, groups and institutions and to provide the benchmarks by which both public and private ordering are gauged. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,101 | 0,107 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,039 |
| Communication savante | 0,017 | 0,022 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,037 | 0,034 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».