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
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.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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