Il Contributo Delle Compagnie Oil & Gas nel Raggiungimento Degli Obiettivi Energetici e Climatici (How Oil and Gas Companies Can Help Meet the Global Goals on Energy and Climate Change)
Bibliographic record
Abstract
Nel settembre 2015, i governi di tutto il mondo hanno adottato17 Obiettivi di Sviluppo Sostenibile (Sustainable Development Goals – SDG) e, pochi mesi dopo – a dicembre – hanno firmatol’Accordo di Parigi. Queste azioni sono la riprova delrafforzamento del consenso globalecirca la necessità di frenare il cambiamento climatico indotto dalle attività antropiche e dipromuovere uno sviluppo sostenibilesu scala mondiale. I due concetti sono infatti strettamente legati: l’urgenza di affrontare il cambiamento climatico va inquadrata nella cornice degli sforzi globali tesi a ridurre la povertà, promuovere la crescita economica, rispettare i diritti umani e di inclusione sociale.\nOn September 2015, governments around the world adopted 17 Sustainable Development Goals (SDGs) and, a few months later – in December – they signed the Paris Agreement. These actions are proof of the strengthening of the global consensus on the need to curb climate change induced by human activities and to promote sustainable development on a global scale. The two concepts are in fact closely linked: the urgency to tackle climate change must be seen in the framework of global efforts aimed at reducing poverty, promoting economic growth, respecting human rights and social inclusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".