Cambio climático y planificación familiar: los países menos adelantados marcan la agenda
Bibliographic record
Abstract
The links between rapid population growth and concerns regarding climate change have received little attention. Some commentators have argued that slowing population growth is necessary to reduce further rises in carbon emissions. Others have objected that this would give rise to dehumanizing "population control" programmes in developing countries. Yet the perspective of the developing countries that will be worst affected by climate change has been almost completely ignored by the scientific literature. This deficit is addressed by this paper, which analyses the first 40 National Adaptation Programmes of Action reports submitted by governments of least-developed countries to the Global Environment Facility for funding. Of these documents, 93% identified at least one of three ways in which demographic trends interact with the effects of climate change: (i) faster degradation of the sources of natural resources; (ii) increased demand for scarce resources; and (iii) heightened human vulnerability to extreme weather events. These findings suggest that voluntary access to family planning services should be made more available to poor communities in least-developed countries. We stress the distinction between this approach, which prioritizes the welfare of poor communities affected by climate change, and the argument that population growth should be slowed to limit increases in global carbon emissions. The paper concludes by calling for increased support for rights-based family planning services, including those integrated with HIV/AIDS services, as an important complementary measure to climate change adaptation programmes in developing countries.
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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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".