Bibliographic record
Abstract
Purpose This paper discusses insights from a study of women working, or seeking or preparing for work, in the information technology (IT) field. At issue is how and whether alternative career pathways and informally acquired skills and knowledge, as well as the operation of gender in learning and work, are acknowledged by employers, colleagues and participants themselves. Design/methodology/approach Using the qualitative technique of life and work history, this study mapped varied learning pathways of women working in the IT field. We used a feminist approach to explore this field, which is characterised as both highly masculine and filled with opportunities for all workers, including women. Findings Juxtaposing categories present in the data, such as female and male, formal and informal education, work and learning, hard and soft skills, and centre and periphery, we establish that binary constructs are both persistent and tenuous. Research limitations/implications Our analysis challenges assumptions about educating the global workforce and the learning pathways within the IT field. Moreover, it suggests the usefulness of further qualitative research on this topic in other geographic locations or fields of work. Originality/value In questioning epistemological and social binaries, our analysis contributes to the re‐theorisation of conceptions of knowledge and learning. In moving from an either/or to a both/and understanding of them, we offer a different way of talking about how they can be understood.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".