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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".