Development Outreach 10 (1) : climate change - low carbon economies and resilient societies
Bibliographic record
Abstract
Contents of the development outreach newsletter are as follows: climate change: low carbon economies and resilient societies, achieving low carbon growth for the world: key elements for a global deal on climate change by Stern, Lord Nicholas, Noble, Ian; low carbon, high hopes: making climate action work for development by Moosa, Mohammed Valli; low carbon growth: our ethical responsibility by Sweeney, James L.; China's move toward a low carbon economy by Xuedu, Lu; Guiyang, Zhuan; and Jiahua, Pan; adaptation activities in India by Ray, Rajasree; old livelihoods in new weather: arctic indigenous reindeer herders face the challenges of climate change by Oskal, Anders; Climate change challenges faced by the inuit by Ford, Violet; Pacific Islands under threat! By Kearney, Geraldine; climate change and insurance markets by Gupta, Arvind; microfinance: climate change connections by Mckee, Katharine; adapting to climate change in Africa: the role of research and capacity development by Denton, Fatima; O'neill, Mary; Stone, John M.R.; Bali climate conference and its main outcomes by Mead, Leila; Gitay, Habiba; Noble, Ian; challenges and opportunities: knowledge for development under climate change by Gitay, Habiba; Nevers, Michele de; knowledge resources; bookshelf; and calendar of events.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.328 | 0.097 |
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".