Analysis of Commitment to the Implementation of Sustainable Public Procurement in Brazil
Bibliographic record
Abstract
Sustainable public procurement is a significant public policy towards sustainable development. In this context, this study investigated the public agents’ commitment to the procurement process within Brazilian public institutions regarding the implementation of sustainable public procurement and their perceptions of organizational and political commitment to the issue. A nationwide quantitative survey was conducted with 201 participants in the planning, selection, or execution phases of public procurement. The findings reveal that individual commitment to sustainable public procurement is moderate, while organizational and political commitment is low or nonexistent. Increased knowledge among public agents and the availability of organizational resources are identified as key factors that could enhance commitment to sustainable public procurement. The results have significant social implications, promoting more sustainable practices in the public sector and supporting responsible development aligned with the Sustainable Development Goals (SDGs). This article is original in addressing sustainable public procurement from the public policy implementation perspective and the individuals’ viewpoint regarding individual, organizational, and political commitment to the issue, providing a deeper understanding of the variables that interfere with the implementation of this policy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".