Green production indicators, a guide for moving towards sustainable development
Bibliographic record
Abstract
This publication is the outcome of a project entitled “Towards a set of indicators for greener production”, co-financed by ECLAC and the International Development Research Centre (IDRC) of Canada, the objective of which was to develop specific knowledge for promoting the design and compilation of harmonized regional indicators on sustainable production and the incorporation of green technologies in firms of Latin America and the Caribbean. The guide should be seen as a set of methodological recommendations for voluntary application. Nevertheless, it is hoped that the use of these guidelines will facilitate the production of data (providing instruments that countries can readily adapt) and enhance their comparability. The production and dissemination of internationally harmonized data on green production will help policymakers in the industrial and environmental areas, as well as businesses and society in general, to understand more thoroughly the environmental processes and practices of firms and allow them to take appropriate decisions for reducing the harmful effects of industrialization, to promote environmentally friendly growth, and to seize new economic opportunities in line with the Sustainable Development Goals (SDGs).
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 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.018 |
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".