Trends and Perspectives of SDG 3 Indicators in Brazil: A Statistical Analysis for 2030
Bibliographic record
Abstract
The objective of this study was to analyze the trend of each standardized indicator for SDG 3 in Brazil, assess the potential for achieving the goals, and evaluate future scenarios for 2030. To this end, data from 40 indicators (28 indicators plus subdivisions) available in the “ODS Brasil Database” and the “SDG Indicators Database” were used. These data were analyzed using monotonic trend analysis, which employed the Mann-Kendall statistical test and Sen’s Slope trend magnitude estimator. The study compares the estimated values for 2030 to the targets set out in the SDGs. Among the indicators assessed (p < 0.05), 5 (12.5%) have the potential to achieve the goals by 2030, 4 (10%) indicators do not have the potential to achieve their goals, and 3 (7.5%) indicators have no conditional goals. However, there is a trend of improving results by 2030. Of the specific Brazilian indicators, only 5 (12.5%) have the potential to achieve the goal defined by the United Nations. The study’s findings support strategic decisions in resource allocation, as they highlight those that have the potential to achieve the 2030 goals and those that require greater attention. These findings can inform the development of more effective public policies, support informed decisions in resource allocation, and enhance governance and transparency in the implementation of the 2030 Agenda.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".