Use of the Dominance-Based Rough Set Approach as a Decision Aid Tool for the Selection of Development Projects in Northern Quebec
Bibliographic record
Abstract
The purpose of this article is to present a summary of research results relating to the application of the dominance-based rough set (DRSA) approach to the selection of projects in the context of the Northern Quebec development plan. Based on this research, decision makers will be able to rank municipalities according to their actual needs in social and economic terms. We believe that public administrators will be able to use various socio-economic indicators in order to classify, based on chosen criteria, municipalities (objects) in one of the following four categories: [A]―the best in the region in terms of the criteria considered; [B]―those that need support in order to acquire category A status; [C]―those that need support in order to acquire category B status; [D]― those ranked lowest in the region and needing special support with regard to the criterion or criteria considered. These four categories are delimited by quartiles relative to the average ranking of municipalities. The chosen criteria are measured in order to provide decision rules based on this classification. These decision rules thus focus on the social and economic needs of municipalities with respect to improving their performance and classification. By targeting these needs, DRSA will help administrators of the Northern Quebec development plan to prioritize actions or to evaluate, for example the social and economic impact of a project in a municipality.
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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 | 0.001 | 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".