Bibliographic record
Abstract
A variety of Inter-State Agreements (ISA) have been developed to establish policies and expectations regarding environmental policy and management. However, governance mechanisms have not been developed to provide for the substantive involvement of Indigenous Nations within States to participate in the development and implementation of these policies.Indigenous knowledge systems, rights, and interests are critical to the development of practical and effective approaches to address complex socio-economic-political issues involved in the sustainable management of effects on the environment.Obstacles and challenges that inhibit the effective engagement of Indigenous Nations are symptomatic of the wider and substantial power imbalances and asymmetries that underlie the relationship with States. Governance of the relationship between Indigenous Nations and States over environmental matters can be improved by: adopting guiding principles to re-invigorate the modalities of collaboration between the Nations and States in mobilizing ISAs; and by establishing a new, permanent governance body, an Intergovernmental Relations Council for the Environment (IRCE) to facilitate and promote formal collaboration in Intra and Inter State-Nation working relationships involving cross-jurisdictional environmental issues involving shared resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.373 | 0.199 |
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".