Norway's Fair Share of Meeting the Paris Agreement
Bibliographic record
Abstract
This report aims to gauge Norway’s fair share of the global response to the climate problem, starting from the recognition that equity is important – in fact, necessary – for addressing climate change. The report focuses on mitigation, although an equitable approach to adaptation is of course equally important. It uses a flexible and transparent framework for equitable effort sharing that is drawn directly from the core equity principles of the United Nations Framework Convention on Climate Change (UNFCCC). The analysis is done using the Climate Equity Reference Calculator, an online tool and database that allows users to select specific equity-related settings relating to responsibility, capacity and other key parameters, and then to use straightforward, standard quantitative indicators to calculate the implied national fair shares of the global mitigation effort. The analysis is based on a range of alternative input selections informed by ethical and empirical considerations that are discussed in more detail within the report. This approach allows the report to contrast Norway's pledged contribution towards the Paris Agreement goals with its fair share, and to articulate what Norway should do in addition to its existing pledge to be in line with its moral obligations.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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; both teacher heads agree on what is shown here.
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".