Justice and the planning process: how far have we come and where are we going?
Bibliographic record
Abstract
Justice is a context-dependent, multi-faceted concept that has historically been associated with planning theory. In this paper, the literature surrounding the concept of justice will be explored to understand where the concept of justice has come from, how it has evolved, and how it can be applied to the planning process. In addition, how justice interacts with other critical concepts, such as the law, morality, and ethics, as well as its ability to function within the institutional context will also be assessed. The concept of justice is be applied to the planning process as it occurs in planning practice in an attempt to bridge the theory-practice gap that exists in planning. Using Fainstein’s concept of justice, with her three criteria of equity, diversity, and democracy, the planning process of the two redevelopments of Regent Park is assessed through the lens of justice in an attempt to apply theories of justice to planning practice.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.048 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".