Bibliographic record
Abstract
We feel it is important to identify research areas that are understudied. Certainly, the impact of economic development on or near Indigenous lands is one such area. In this issue, we have three feature articles that approach the issue of economic development in terms of policies, impacts, and benefits. What is clear is that we need a much better understanding of the benefits and perils that come with development. We also need better cooperation between governments, corporations, and Indigenous communities in order to develop effective legislation and fair land use and resource agreements. IIPJ wishes to encourage those who have studied development issues to submit their work. We will shortly be announcing our intent to create an international database of quality research across a wide range of important subjects. The purpose will be to create an ever expanding, open-access source of research. This database may also contain examples of agreements and rigorous assessments of the projects in order to advance our understanding of the challenges that come with economic development.
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 | 0.041 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.311 | 0.183 |
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".