Bibliographic record
Abstract
Action Against Hunger is an international non-governmental organization with six headquarters located around the world that focus on ending hunger in low and middle-income countries. The Canadian office uniquely provides evidenced-based technical support and evaluation for headquarters focused on implementing interventions and programming to mitigate hunger. For my practicum I worked in the International Gender Unit to support ongoing policy development and updating of the organization’s cross-network gender policy. Gender inequalities have direct causal links with malnutrition; yet, in March 2020 the Global Nutrition Report showed that global efforts to mitigate hunger by addressing gender inequalities are behind on most targets. To better capture and learn how to address underlying inequalities and drivers of malnutrition, my practicum research focused on the associations and non-associations between gender, gender-based violence, and malnutrition. This practicum placement had three objectives: 1) to provide a literature review 2) to provide a database comprised of peer-reviewed and grey literature; and, 3) to support new policy development during cross-headquarters discussions, research, and reporting. During this placement I had the opportunity to work online with individuals across five continents and twenty-one countries. This included facilitating break-out policy discussions during policy meetings, as well as semi-structured interviews that were conducted prior to providing a literature review and socio-ecological discussion on gender, gender-based violence and malnutrition. The opportunity to engage in international and cross-cultural collaborative work has been the highlight of my practicum. It has provided the opportunity to not only sharpen my reflexive praxis as a student of public health, but to sharpen my understanding of the policy process at the organizational level. It has additionally illuminated the importance of structural and social contexts in public health research and programming, especially within efforts to address gender inequalities and gender-based violence associated with malnutrition.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".