A framework for assessing the procedural justice in integrated resource planning processes
Bibliographic record
Abstract
Globally, there is growing competition for a wide range of natural resources.The need to manage and allocate natural resources fairly has been identified as an important policy goal in many discussions.These deliberations have in turn brought questions of social justice into sharp focus.To better understand justice issues in resource allocation and planning, general theories of social justice are reviewed from various perspectives.Social justice focuses on creating fair and equal conditions in which individuals matter, and their rights are recognized and protected when decisions are made.Social justice is discussed in the literature with respect to three main concepts: equity, distributive justice, and procedural justice.Our review of this literature reveals that attending to procedural justice can lead to a process ensuring a fair allocation of resources, adding transparency, and improving public acceptance.However, the procedural justice literature lacks a comprehensive model to assess integrated natural resource planning processes visà-vis procedural justice.This paper addresses this gap.It describes a model of procedural justice which we propose is well-suited for application in a range of circumstances and across jurisdictions.To generate the model, several theories of procedural justice are reviewed, leading to the identification of five fundamental principles against which processes for making resource management and allocation decisions can be assessed.To ensure fairness, planning processes should have (i) an unbiased framework; (ii) an informative procedure; (iii) a process that secures legitimate representation; (iv) an effective public consultation process and (v) the ability to resolve conflicts.
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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 | 0.058 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".