Land in Conflict: Managing and Resolving Land Use Disputes: Chapter 1
Bibliographic record
Abstract
Every day, public officials must make challenging decisions involving land that impact open space, economic development, transportation, and countless other issues. These decisions may affect the built environment, the landscape, the quality of life, and the economy for decades or even centuries. How officials make these decisions influences the way community members interact with one another and whether they work as a cohesive or a divided group. When faced with complex decisions, communities often become embroiled in battles that tear at the civic fabric, pit neighbor against neighbor, demonize the applicant, and wear down local officials. Volunteer board members, neighbors, and applicants are often disheartened by what seems to be an insufficient process for solving these difficult, heated land use disputes. The authors have used a mutual gains approach to manage the most challenging decisions. This approach is guided by core principles, follows a set of clear action steps, and is useful at different stages of land use decision making. It is different from, though not incompatible with, the required land use procedures. The mutual gains approach to preventing and resolving land use disputes is not a single process or technique. It draws from the fields of negotiation, consensus building, collaborative problem solving, alternative dispute resolution, public participation, and public administration. The result is a more public, collaborative process designed to tease out the range of interests and criteria, compare various alternatives, and determine which alternatives meet the most interests. Chapter 1 introduces the approach and gives an overview of case studies from across the United States and Canada that illustrate the principles and steps in the mutual gains approach.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".