Development context influences fertility and reproductive health decisions in Greenland amid economic and environmental change
Bibliographic record
Abstract
Abstract Greenland is experiencing profound effects of climate change that impact hunting and fishing economies and livelihoods for Kalaallit, the Inuit people of Greenland. While environmental change has indirectly influenced fertility outcomes in hunting-dependent communities of North Greenland, little is known about the role of climate change in shaping fertility decisions elsewhere in the country. Given existing fertility and reproductive health disparities among Kalaallit individuals relative to other Arctic populations, understanding how climate change may interact with social, economic, and cultural conditions that influence reproductive health is critical. This study explored how social, economic, and environmental factors influence fertility decisions in Greenland. Grounded in principles of community-based participatory research, we conducted 35 semi-structured interviews with reproductive-aged (18–49 years) men (n = 16) and women (n = 19) from two communities, examining the social, cultural, environmental, and economic resources that shape their fertility decisions. Findings indicate that improving community capacity to address housing, education, and economic inequities is essential for supporting individual fertility and reproductive health, irrespective of climate change impacts. While participants widely observed climate change effects, they expressed confidence in Kalaallit ability to adapt and capitalize on benefits of climate change. This study highlights the importance of addressing development disparities to improve reproductive health outcomes, as well as climate adaptation in Greenland.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".