Mapping of scientific production around the sustainable development goals - SDGS and food production
Bibliographic record
Abstract
ABSTRACT: This research aimed to understand the subject regarding the 2030 Agenda Sustainable Development Goals and food production through literature reviews covering the timespan from 2001 to 2021. Methodologically, this paper is framed as a literature review and uses the Scopus platform to get to the database, selecting 243 papers. The results pointed out that the United States presented the highest number of published documents (126). When it comes to the origin of affiliation, the universities located in the Netherlands (University of Wageningen) and Canada (University of Guelph) present together the highest number of published documents (24). Among the keywords, the most frequent are food safety, followed by the food supply, sustainability, and climate change. Finally, regarding the main subjects tackled during the analyzed time period, it was verified that the issues concerning public policies, land use, and food safety were discussed throughout the entire timespan. In turn, the most recent period, mainly covering the articles published from the year 2021 on, inserts into the discussion agenda the aspects of Covid-19, the pandemic, and its impacts, especially influenced by the setting experienced worldwide. At the same time, other subjects less discussed took the spotlight, also emerging from the Covid-19 pandemic, such as digital agriculture and digital technology, which began to have major relevance. At last, it is possible to infer that reaching sustainable development goals is even more challenging under a pandemic context experienced by all the countries from 2020 to the present day.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".