Siting Major Public Facilities: Facts, Values, and Accountability
Bibliographic record
Abstract
Government agencies and other organizations responsive to a diverse constituency face enormous challenges in identifying priority sites for relocation, expansion, or new development. Of paramount importance is establishing transparent decision processes that reach accountable, defensible, and wise outcomes. Unfortunately, documented examples of successful approaches to evaluation, prioritization, and site selection are scarce. The purpose of this paper is to both offer a descriptive case study and an intellectually rigorous, fundamentally practical “best practice” approach for identifying priority sites. By employing value-focused thinking and decision analysis techniques to a complex site selection problem, we present a way to address common challenges such as potential technical and nontechnical knowledge conflicts, distinguishing between “facts” and “values,” incorporating uncertainties, generating criteria weights, making trade-offs and building consensus across interests. Our approach is contextualized in a Canadian government case study of relocating a $300 million dollar facility.
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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 | 0.001 | 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 | 0.001 |
| 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".