Gender and Information Technology: Implications of Definitions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines implications of definitions of information technology to women's participation in the industry and in academe. It is exploratory only, based on a review of selected government and industry reports and data related to IT education and the profession. However, it argues that there is evidence to suggest that discourse related to information technology has the effect of excluding women and multi-disciplinary perspectives. On the one hand, there is considerable evidence that the IT industry and skills it demands are multi-disciplinary and that many people working in the industry, particularly women, come from a variety of disciplines. On the other hand, despite the evidence of the multidimensional nature of IT, the impact of convergence, the importance of matching IT solutions to user needs and so on, a very narrow definition of IT dominates the discourse. This definition equates IT and IT professionals with computer science and engineering, disciplines which are predominately male. The result, then of this narrow definition is to marginalize women and their contributions. This is a pattern that has been observed with the development of other disciplines such as medicine. Not only does the narrowing of the definition of Information Technology tend to exclude and devalue the contribution of women but it also results in marginalization of other disciplines that would bring more “neutral” or “critical” perspectives to bear on technology. Thus the exclusion of multiple disciplines and women may contribute to poor technology decision-making at the societal and organizational level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it