Gender Differences in an Austrian IT Manufacturing Plant
Bibliographic record
Abstract
Despite the gains women have made in the last three decades, a large body of research has recently emerged suggesting that major economic changes occurring on a global scale are having detrimental consequences for women’s labor-market position. At best, these developments are judged to likely limit further progress toward gender equality (Human Resources Development Canada [HRDC], 2002). While in industrialized countries the manufacturing and primary industries have declined, the service sector, where women have traditionally been concentrated, has grown quite substantially. The service sector is highly heterogeneous, encompassing both well-paid professional and technical occupations as well as low-skill, poorly paid occupations. A stratum of highly skilled, high-status workers has emerged, coupled with a large mass of technically semiskilled or unskilled workers who acquire their training on the job or in short courses lasting a few weeks (Standing, 1989). Wage polarization has accompanied the growing demand for highly skilled workers and declining demand for unskilled labor. Increasingly, the workforce is segmented into a primary labor market offering good wages, job security, and opportunities for advancement, and a secondary labor market of low-paid, contingent workers (Economic Council of Canada [ECC], 1991). Women, and especially visible minority women, remain overrepresented in the latter. Much of the literature on gender differences in the IT workforce has focused on the high-end IT jobs. Relatively little is known about low-end IT jobs and the role of gender. The IT industry is mainly a service-oriented industry. However, many of the tools used in these services have to be manufactured by IT manufacturers. In this study, we examine gender differences in the working conditions (job and organizational characteristics, and quality of working life [QWL]) of employees in a chip-manufacturing plant.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".