The Interaction between Self-Esteem, Perceived Gender Discrimination and Employment Motivation: A Log-Linear Analysis
Bibliographic record
Abstract
This study analyzes the relationships between self-esteem, perceived gender discrimination, and employment motivation. Results show that individuals not perceiving gender discrimination are 3.66 times more prevalent than those who do. Individuals who see the purpose of employment as a way to improve themselves are 1.8 times more motivated than those who want to contribute meaningfully to society. Self-improvement is a stronger motivator than contributing to society, and income-focused individuals show 2.8 times higher motivation. Those with low self-esteem aspiring to contribute to society are 2.6 times more likely to be motivated than those focusing on self-improvement. Achieving gender equality and preventing discrimination can enhance personal development and societal contributions, leading to increased individual success and social welfare. The motivations of self-development and usefulness to society make the strongest contribution to an individual's self-esteem. These motives are related to inner satisfaction and social recognition.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".