Collaboration among sectors to increase pulse consumption
Bibliographic record
Abstract
The United Nations declaration of 2016 as the International Year of Pulses (IYP) provided an unprecedented opportunity to showcase pulses on the global stage for their contribution to affordable nutrition, health, and sustainability. Despite the IYP's successes in stakeholder engagement, continuing to foster and strengthen partnerships and collaborations is necessary to meet the IYP goals of increased pulse production and consumption for human benefit. Shifting consumer behavior to increase pulse consumption emerged during IYP meetings as a shared priority for all stakeholders. Focusing on this shared priority provides an opportunity to strengthen collaboration among all stakeholder groups for research, education, marketing, and ingredient/food production. Although the IYP officially closed at the end of 2016, the pulse community has an opportunity to continue building successful collaborations. The future research agenda can foster increased pulse production and consumption to address global nutrition, health, and sustainability challenges, provided that it is developed with multisectorial perspectives and cross-disciplinary collaborations. But, most importantly, the research agenda for pulses must be centered more deliberately on the end consumer and how to drive shifts in behavior toward increased pulse consumption, as this is the common shared priority around which all stakeholders can rally.
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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 | 0.038 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".