A social work study to investigate the relationships between women’s personal characteristics and employment status
Bibliographic record
Abstract
Women play important role on building a sustainable family oriented society; they could also contribute to society by contributing to labor market.However, women's personal characteristics such as educational background, years of experience, etc. could impact their future occupations.In this paper, we study the impact of various factors on women's job status.The measurement tools for social factors of employment in this research is a questionnaire consists of 32 questions.The study measures the reflection of repliers to different social factors including social position, popularity, socialize, social manners, self-reliance, speech abilities, responsibility, etc.Data were gathered from a sample of 300 people using random sampling and analyzed using descriptive mono factor statistics, Spearman correlation, Kramer correlation coefficient, Chi-square, regression and path analysis.The validity of questionnaire is tested by using Cronbach alpha (%75).The results indicate that there are some meaningful relationships between woman's educational level, age, residency status, socialization capability, urbanity, skill & ability and their employment.The study, however, does not find any relationship between marital status and number of children and woman's income with employment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".