NDC-SDG Connections: Data on updated NDC submissions (V2)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.122 |
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 it