Determinants of the sustainable development goals disclosure on websites of Portuguese Municipalities
Bibliographic record
Abstract
This paper aims to analyse the Sustainable Development Goals (SDG) related-disclosure practices on the internet, as well as to identify the main drivers of SDGs e-reporting in Portuguese municipalities. A qualitative methodology was adopted through the content analysis of all the 308 Portuguese municipalities’ websites. Based on theoretical assumptions of legitimacy and stakeholder theories, we associate the SDGs e-reporting with some municipalities’ characteristics (such as location and size) as well as with the use of ODSLocal platform. Our findings indicate that only a quarter of municipalities refer to the SDGs in their websites. None of the presidents’ messages mention the SDGs. Only 10 entities have a separated tab on the website to disclose information related to the SDGs. Most information is generic and not about specific SDGs. Almost 64% of the disclosing municipalities disclose information related to SDG in the news. Looking to some contextual factors, the results indicate that coastal and larger municipalities are more likely to disclosure about SGDs; on the other hand, the use of ODS local platform does not seem to influence.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".