NDC-SDG Connections: Data on updated NDC submissions (V2)
Bibliographic record
Abstract
NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development. The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs. With its second version, the NDC-SDG Connections project opened its data for public re-use. The data on the updated NDC submissions (V2) is provided in the following formats: single .csv files (per data per SDG) zip .csv file (data per SDG for all SDG in one zip) .xlxs file Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info Additional files: .pdf file documenting the methodological framework including the coding and data validation process of the NDC-SDG Connections project .csv file with all NDCs included into the analysis (V2) Note:This data set contains data for second NDC submissions (V2). The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data. Updated NDCs included in this first version of V2: Angola, Antigua and Barbuda, Armenia, Australia, Bahamas, Bahrain, Bangladesh, Bhutan, Bosnia and Herzegovina, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, Colombia, Comoros, Costa Rica, Cote d’Ivoire, Cuba, Dominican Republic, Eswatini, European Union, Fiji, Gabon, Ghana, Georgia, Guinea, Haiti, Honduras, Iceland, Jamaica, Japan, Kenya, Lao People’s Democratic Republic, Lebanon, Malaysia, Maldives, Marshall Islands, Micronesia, Monaco, Mongolia, Morocco, Mozambique, Nauru, Niger, Peru, Republic of Korea, Rwanda, Saudi Arabia, Serbia, Seychelles, South Africa, State of Palestine, Switzerland, Timor-Leste, Tonga, Uganda, Uzbekistan, Zambia.
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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 | 0.021 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.034 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.427 | 0.332 |
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".