Evaluating women’s happiness levels with ARASsort: The case of Türkiye
Bibliographic record
Abstract
The happiness levels of women exhibit variations attributable to a myriad of factors, encompassing economic, social, cultural, and demographic variables. Numerous governments incorporate the measurement of happiness levels as part of life-satisfaction analyses; nonetheless, these analyses lack a comprehensive framework for predicting happiness levels over specific periods. Notably, in developing countries, women confront the adverse consequences of economic, social, cultural, and demographic determinants to a greater extent than men. Paradoxically, they remain significantly underrepresented in both academic and industrial domains. In light of this, the primary objective of this study is to conduct an in-depth analysis of happiness levels and their underlying determinants from a gender-oriented perspective. Therefore, the pertinent literature has not dedicated a systematic approach to classify and forecast the happiness of women. The present paper initiates by elucidating the factors influencing women's perceptions of happiness through a comprehensive review of the existing literature. Then, a multiple attribute decision-making algorithm-based sorting methodology, ARASsort, is utilized to evaluate how women’s happiness levels are affected by life satisfaction components in a developing country, Türkiye. The selection of ARASsort is based on its performance over other traditional sorting approaches in terms of time and effort attachment. Various factors affecting the happiness levels of women in different cities in the country sample were discussed and analyzed in detail in accordance with the main findings of the OECD Better Life Index (2020), through representative data selected from TÜİK's life satisfaction dataset.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".