COP27: The Prospects and Challenges for the Middle East and North Africa (MENA)
Bibliographic record
Abstract
In line with the global trend, the Middle East and North Africa (MENA) region has been growing vulnerable to the direct and indirect health effects of climate change including death tolls due to climatological disasters and diseases sensitive to climate change since the industrial revolution. Regarding the limited capacity of MENA countries to adapt and respond to these effects, and also after relative failures of the previous negotiation in Glasgow, in the upcoming COP27 in Egypt, the heads of the region's parties are determined to take advantage of the opportunity to host MENA to mitigate and prevent the worst effects of climate change. This would be achieved through mobilizing international partners to support climate resilience, a major economic transformation, and put health policy and management in a strategic position to contribute to thinking and action on these pressing matters, at least to avoid or minimize the future adverse consequences.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".