Welcome from the International Union of Nutritional Sciences
Bibliographic record
Abstract
As President and Secretary General of the International Union of Nutritional Sciences (IUNS), we welcome and applaud the extensive scholarly work presented in this special issue of Maternal and Child Nutrition produced by the IUNS Task Force on Traditional, Indigenous, and Cultural Food and Nutrition, and for which the IUNS Council has provided support for editorial and publication costs. This issue addresses important concepts of international nutrition that are particularly pertinent with the increasing globalization of the world's food supply and the spectre of climate change that affect biodiversity, food security, and the marginalization of Indigenous and Tribal Peoples. The interlinking aspects of gender roles, biodiversity, and food security are researched and discussed for their impact on nutrition and health within several unique societies in different global regions. The IUNS Task Force has for many years, over three IUNS cycles, systematically addressed ways to understand how local and traditional food of Indigenous and Tribal Peoples contributes to well-being. Many outstanding researchers developed their local teams to collect and analyse data in partnership with the Centre for Indigenous Peoples' Nutrition and Environment (CINE) at McGill University (Canada), the IUNS, and the United Nations' Food and Agriculture Organization (FAO) for publications described in this issue. We wish especially to recognize three esteemed colleagues of the IUNS who contributed to this overall program whom we have lost since the start of the work of the Task Force: Dr Elizabeth Chinwe Okeke (Nigeria), Dr Lois Englberger (Federated States of Micronesia), and Dr Gail Harrison (USA). Their stellar contributions, which are cited in Dr Kuhnlein's article in this publication, are found in the 2009 and 2013 publications from the Task Force. On behalf of the IUNS, we express our appreciation to the Task Force for a fascinating collection of articles that adds more interdisciplinary sparkle to the work of the IUNS. August, 2017
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".