Designing an argumentative decision-aiding tool for urban planning. AIPA : an interface between multicriteria decision aiding and argumentative frameworks
Bibliographic record
Abstract
Urban planning is an important issue for all cities. In order to meet the challenges of sustainable urban planning, we propose a participative decision-support tool that allows stakeholders to engage jointly in structuring a decision process, to identify alternatives, to construct criteria, to challenge their relevance and to evaluate them. The novelty in our approach is the use of an argumentative approach to support multicriteria decision aiding. The use of an argumentative framework allows the stakeholders to formalize the decision problem by taking explicitly into account the diverse opinions expressed and ensuring their traceability. Through the argumentative approach, our goalis thus to enhance participatory decision making by organizing and formalizing debates between stakeholders. To this effect, we propose AIPA, an interface the makes the transition between natural language and abstract argumentation systems. Our aim is to place debates at the center of the decision analysis process in order to facilitate the acceptance of the final decision by all parties.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".