Gender Identity, the Culture of Organizations, and Women's IT Careers
Bibliographic record
Abstract
The decreasing number of women in information technology (IT) programs and careers has received increasing attention over the last decade (Arnold & Niederman, 2001; Camp, 1997; Cukier, Shortt, & Devine, 2002; Klawe & Leveson, 1995; Niederman & Mandviwalla, 2004). The proliferation of technology innovations over the last 20 years has made the computer less of a mystery to the general public and placed it in a more prominent place in both the office and home. The integration of networks and the placement of the personal computer as a new artifact in society has signaled both cultural as well as technological changes for the future (Woodfield, 2000). However, bigger transformations are yet to emerge. The future efforts of technology will focus on areas such as artificial intelligence, robotics, and bio-technology and implications are significant. Yet, despite these major changes, organizational cultures across businesses appear to have retained their masculine bias or feel. If the current trend of under representation of women in the IT field continues (Camp, 1997; Klawe & Leveson, 1995; MacInnis & Khanna, 2005), these future developments will be without the influence of women, and IT will become entrenched in the public psyche as a masculine pursuit (Woodfield, 2000). The purpose of this article is to present an overview of organizational culture and its influence on gendering identities. Further, an exploration of the evolution of organizational culture within the IT discipline will be offered to assist with our understanding of why fewer women are pursuing IT careers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".