Extractive Resource Governance: Creating Maximum Benefit for Countries
Bibliographic record
Abstract
In April of 2013 The School of Public Policy hosted a three-day, invitation-only symposium to discuss best practices in extractive-resource governance. The symposium, which consisted of five panel sessions and three keynote addresses, fostered an open debate and lively interaction among participants from university, industry, government and nongovernmental organizations. By providing a neutral and open platform for discussion, the symposium’s primary goal was to build common lines of communication around policy gaps related to fiscal governance, regulatory frameworks and community development in extractive-resource-producing jurisdictions and to develop strategies for bridging them. Twelve countries were represented, including: Albania, Australia, Canada, Colombia, Indonesia, Israel, Ghana, Nigeria, Norway, the Republic of Congo, the United Kingdom and the United States. This paper encompasses the leading thoughts and ideas discussed at the symposium and highlights points of general consensus or conflicting views. It concludes by introducing the Extractive Resource Governance Program, developed by The School of Public Policy to provide regulatory and policy education, research and analysis to jurisdictions with emerging or established extractive 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".