Rethinking Resource Management: Justice, Sustainability and Indigenous Peoples
Bibliographic record
Abstract
Part I: Introduction (and Disorientation) 1: Worlds Turned Upside Down Part II: Ways of Seeing 2: The Problem of 'Seeing' Part III: Ways of Thinking 3: Complexity in Resource Management Systems: Conceptualizing Abstractions and Internal Relations 4: Beyond 'Negotiation': Rethinking Conceptual Building Blocks 5: Reading Landscapes: Cartesian Geographies or Places of the Heart? 6: Ethics for Resource Managers Part IV: Case Studies 7: Case Studies: A Tool in Research for Resource Management 8: Recognition, Respect and Reconciliation: Changing Relations Between Aborigines and Mining Interests in Australia 9: Dependent Nations or Sovereign Governments? Treaties, Governance and Resources in the USA 10: Indigenous Rights of States' Rights: Hyrdopower in Norway and Quebec Part V: Ways of Doing 11: Diversity and World Order: Professional Practice and Resource Managers 12: Social Impact Assessment 13: Policy Arenas: Reform, Regulation and Monitoring 14: Co-Management of Local Resources Part VI: From Theory to Praxis 15: Sustainability, Equity and Optimism
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".