Bibliographic record
Abstract
Edi tor's Intro duc tionWe offer a new sec tion in this issue of the Jour nal of Aborig i nal Eco nomic Devel op ment that will remain a reg u lar fea ture: the research note.It is our inten tion to sup ple ment the inno va tive research pro duced in this jour nal by offer ing a wider range of inquiry through pub lish ing research notes and/or work ing papers.In this, the first research note pub lished, Soma Dey, lec turer in the Depart ment of Women and Gen der Stud ies, Uni ver sity of Dhaka, Ban gla desh, assesses the impact of new agri cul tural tech niques on the indig e nous Garo pop u la tions of the Modhupur Garh for est in Ban gla desh.In par tic u lar, Dey high lights how the estab lish ment of regional trans por ta tion net works led to the insin u a tion of cash crop cul ti va tion to the det ri ment of thriv ing sub sis tence econ o mies in the once remote Modhupur Garh.Accord ing to Dey, research such as hers is required to better under stand "how var i ous aspects of Garo soci ety have been impacted by this slow shift from a sub sis tence econ omy to par tic i pa tion in the dom i nant com mer cial econ omy," events that no doubt will res o nate with Aborig i nal lead ers in Canada who have and con tinue to face sim i lar issues.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.273 | 0.151 |
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".