Transdisciplinarity and Its Challenges: the Case of Urban Studies
Notice bibliographique
Résumé
This contribution clarifies the distinction between unidisciplinary, multidisciplinary, interdisciplinary and transdisciplinary research about environment and human behaviour. One objective is to consider the challenges and opportunities transdisciplinarity offers in terms of the emergence of new ideas for theory and application. The costs and benefits, as well as the advantages and constraints of a transdisciplinary approach in the field of urban studies are then considered, and compared with multidisciplinary and interdisciplinary approaches. First, a brief history of the concept of transdisciplinarity is presented. Second, the scientific context (the unit of analysis, application and theoretical goal) is identified. Third, conclusions are drawn about the perspective that researchers need to adopt if a transdisciplinary approach is to be effective (looking for coherence versus paradoxes). All of these reflections on transdisciplinarity are supported by the research experience gained in studies on Canadian (Quebec) and French (Strasbourg) suburbs. The paper focuses on the representation and perception of urban space, using the concept of legibility. The study of the legibility of urban space requires data that are extremely different in terms of format, and consequently different in terms of underlying collection methods. For example, the nature of the one’s social network (frequency of visits, spatial distribution, type of relations, etc.) represents a corpus of information whose format is different from that pertaining to a person's attachment to a place (type of place, perceived quality, emotional origin of the attachment, scale, etc.). In addition, this information is different from the symbolic, economic, functional or other values attributed to frequented places, and from internalized spatial relationships between known or frequented places. However, each of these pieces of information is important in order to understand how an individual constructs a cognitive image of a given urban space (mental cartography) and to explain the structure of this image (mental map). If we take these elements separately, we observe the incidence of numerous factors on the construction of a mental representation. Conversely, we are incapable of articulating these different factors, and of understanding their respective importance depending on the situation. In other words, we are incapable of understanding the phenomenon in its complexity, because we do not confront the different models that underlie all these elements of knowledge.In the end, if our reasoning is extended further to the necessary absence of unity of knowledge, then transdisciplinarity does not produce any knowledge other than that which results from the articulation of existing knowledge. Then, we need to distinguish articulation, the main process of transdisciplinarity, to relationship. This distinction can be described as follows: through relationship, one seeks a reality that is common to the different entities that make up the object, whereas through articulation, one aims for the coherence of multiple levels of reality that make up an object.
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 | 0,085 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,008 | 0,013 |
| Études des sciences et des technologies | 0,033 | 0,125 |
| Communication savante | 0,033 | 0,032 |
| Science ouverte | 0,005 | 0,036 |
| Intégrité de la recherche | 0,012 | 0,013 |
| 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 ».