Regarding the UN Sustainable Goals of Well-Being, Gender Equality, and Climate Action: Reconsidering Reproductive Expectations of Women Worldwide
Bibliographic record
Abstract
Climate action represents the most comprehensive of the 2015 United Nations 17 Sustainable Development Goals (SDGs) in that climate change impacts all other goals. Urban overpopulation is a primary cause, as energy consumption is a significant source of carbon dioxide emissions directing climate change. The population increase origin is attributable to the agricultural/urban developments that became geographically widespread approximately 6000 years ago. Simultaneously, religious belief stressed multiple children, with women obligated to produce them. This female duty created gender inequality and reduced the health and well-being of women, as pregnancy is a noted risk factor for decreased lifetime health. Regardless of the detrimental risk to their health and well-being, the gender inequality, and the adverse effects of birthing multiple children regarding climate action, women today continue to feel obliged to reproduce appropriately. This burden requires change to meet the three sustainable development goals of good health and well-being (SDG 3), gender equality (SDG 5), and climate action (SDG 13). An author-developed mindfulness-based psychoanalytic narrative research method presents a means for promoting such change based on a qualitative narrative analysis of the responses of several participants regarding its success in clarifying the values of these women in overcoming career-related burnout.
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.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".