Transdisciplinarity and Its Challenges: the Case of Urban Studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".