Development Outreach 10 (1) : climate change - low carbon economies and resilient societies
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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