Breaking through the silicon wall: gendered opportunities and risks of new technologies
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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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