Breaking through the silicon wall: gendered opportunities and risks of new technologies
Bibliographic record
Abstract
Technology design and development has traditionally been characterized by a lack of attention to women’s priorities and activities; a lack of analysis of gendered impacts; and the influence of socio-cultural gender norms that position technology as a male pursuit. Advances are seen, but progress continues to be slow. For example, women are highly-represented in biology globally, but participation drops significantly in computational biology, and digital gender gaps in ownership and information and communication technology skills persist. The term “silicon wall” calls attention to the constraints faced by women and under-represented groups in the design, implementation, and appropriation of new technology. At the same time, the acceleration of technology-driven development poses new risks, in the form of AI and digital-based monetary systems, for example. These trends may reverse momentum in gender equality and empowerment through effects on labor force participation and economic opportunities, health and wellbeing, and (lack of) financial inclusion. Steps need to be taken to address gaps, constraints, and lack of opportunities that penalize women and underrepresented groups, in order to break through the silicon wall. This article builds on a forthcoming UNCTAD report to assess the intersection of digital technologies as they intersect with gender, diversity in the technology workplace, and development, in order to understand risks and opportunities for innovation and implementation of new technologies.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".