Encouraging Girls to Consider a Career in ICT: A Review of Strategies
Bibliographic record
Abstract
This article reviews strategies designed to improve female participation in ICT studies and careers.In reviewing a range of strategies from around the world, the article identifies the different actors engaging with the "problem" of girls and technology.It points to the many crossovers that occur as governments, higher education providers, industry, and the voluntary sector complement each other in their search for effective solutions to a dilemma that is increasingly recognised as being much more complex than a simple dichotomy of gender and technology.The particular milieu for which this review has been conducted -an educationally and otherwise disadvantaged area of Melbourne, Australia -is reflected in identification of strategies specifically focused on girls and women with low socio-economic status (SES) and students exposed to educational disadvantage.The first section of this article -an introduction to and overview of the global problem of female under-representation in ICT -provides a context in which the problem, as it exists in Australia, may be understood.Section two consists of four sub-sections in which various types of intervention strategies are described.The sub-sections: Government/Policy-Driven Activity, Education Institutions/Research Activity, Industry Groups, and Voluntary Initiatives indicate the breadth of effort being employed in pursuit of solutions to the "problem" of girls and technology.They point also to considerable overlaps in design, delivery, and results, and illuminate many of the problems encountered along the way.The third and final section of this article discusses the pros and cons of the various types of approach, comparing and contrasting the efforts of government, educational institutions, industry-based groups, and voluntary initiatives in their search for successful and sustainable ways of attracting girls to ICT studies and careers.This article brings together in one place a variety of strategies aimed at improving female participation in ICT studies.In doing so, it highlights the difficulty of one-size-fits-all approaches and illuminates the importance of front and back-end processes to the delivery of successful programs.It demonstrates that for those designing programs capable of attracting girls to ICT, failing to prepare is preparing to fail.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".