Why do Indigenous peoples food and nutrition interventions for \nhealth promotion and policy need special consideration
Bibliographic record
Abstract
In MemoriamDr Lois Englberger was the academic partner in the Pohnpei case study presented in this volume, until her untimely death in 2011.As a "citizen of the world" Lois travelled and worked in India, Colombia, Yemen, the Kingdom of Tonga and several other Pacific island nations.Her work with local collaborators in the Federated States of Micronesia developed the Island Food Community of Pohnpei, which has been praised and supported by a breadth of government ministries in the country, the Pacific region, and internationally.This volume is dedicated to this remarkable woman, who was an essential inspirational member of our team. Indigenous Peoples'food systems & well-beingFood and agriculture Organization of the united nations & Centre for indigenous peoples' nutrition and Environment Rome, 2013the designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and agriculture Organization of the united nations (FaO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.the mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FaO in preference to others of a similar nature that are not mentioned.the views expressed in this information product are those of the author(s) and do not necessarily reflect the views or policies of FaO.isBn 978-92-5-107433-6
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".