Implementing Transdisciplinarity: Lessons from Research and Practice
Bibliographic record
Abstract
This communication is about implementing transdisciplinarity in Environment-Behaviour studies. The work of the Interdisciplinary Research Group on Suburbs (Groupe interdisciplinaire de recherche sur les banlieues, GIRBa, in French) at Laval University (Canada) is presented to illustrate the theoretical premises and methodological strategies that define the group’s work and orientations as transdisciplinary. GIRBa is a group of academic researchers in the fields of architecture, urban design, planning, as well as human and social sciences; it includes professors, post-doctorate and doctoral candidates, Masters’ students in sociology, urban design and architecture, as well as undergraduates in architecture. All members share an interest for retrofitting post-war suburbs and the conviction that knowledge about people-environment relations is essential to support design and planning. Over the last five years, GIRBa has been conducting research along three main lines to inform and orient the future of Quebec City’s post-war suburbs: 1) suburbs’ morpho-genesis, urban morphology and architectural typologies; 2) residents’ uses and meanings of dwellings, neighbourhoods and broader metropolitan area; 3) policies, regulations, ideologies, as well as planning theories and practices. GIRBa’s program consists of an iterative process between scientific research, action research and design research. Research mandates from various government offices contribute in feeding the team with pragmatic research questions and favour action research. Architectural and urban proposals emerging from this process lead to new theoretical reflections that can, in turn, modify them. In this respect, GIRBa’s research problems, objectives and strategies are in constant redefinition, taking advantage of an action-retroaction process. Our research program and collaborative planning process both produced a rich and unique knowledge. Combined, they have led to a better understanding of the complexity of suburban settings, of the challenges facing them and, most of all, of avenues for action. GIRBa’s work also exemplifies how universities can play a critical and essential role in training professionals and researchers to work together providing beyond their specific disciplinary competencies. Based on five years of trials and errors in implementing transdisciplinary research, this communication reports on its difficulties and successes, on the strengths and weaknesses of such research programme, as well as on the type of research agenda, research process and team composition favouring such research. A discussion of the limits and challenges of transdisciplinarity concludes the presentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.287 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.024 | 0.125 |
| Scholarly communication | 0.042 | 0.062 |
| Open science | 0.010 | 0.039 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".