Bibliographic record
Abstract
Globalization has many positive and negative effects on national economics worldwide such as poverty eradication, availability, technology, foreign investment, terrorism, job and price instability, and currency fluctuation.As well one fundamental effect of globalization is enhanced demand for education.Some people who research globalization believe realizing such as a demand will ameliorate the wellbeing and life of and provide more job opportunities to citizens especially women.This paper will critically investigate the effects of globalization, either positive or negative, on women's education.However, in particular, this article will examine hidden and unhidden issues: 1) globalization raising the rate of return to women's education, 2) a worldwide movement for women's rights, 3) increasing demand by women for higher education levels, and 4) women still being discriminated against in labour markets.Eventfully, the results are mixed and the situation is complex.Payoff for women's education has been resulted by trade liberalization and economic globalization (e.g., women taking advantage of increasing demand for educated workers).Even though after the emergence of globalization women have been able to find more job positions, their work type and career prospects are often worse than those of men.Moreover, the connection between women's education, well-being, and economic position is still unclear.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".