Bibliographic record
Abstract
ABSTRACT This qualitative study explores the narratives of 39 "late-entry women" from across Canada as we recount our doctoral journeys. It considers the challenges we are confronted with as we face a generally accepted cultural consensus that we do not seem to belong in academia. The research is positioned within qualitative research methods, namely, narrative inquiry – including photovoice, narrative writing workshop and individual interviews. These methods helped late-entry women articulate what is helpful to our success and acceptance in the university. As a late-entry student myself, I had an abiding interest in sharing these women's stories and was touched by many of the themes that surfaced during the research. As such, my insider status – my identification with my participants, helped me to uncover their stories of feeling marginalised, even at times feeling invisible in the academy. Because of others' comments, some wondered about the value of a newly minted PhD at a later stage in life; and some were tired of the constant need to defend or explain our reasons for returning to school at a later stage in life. These feelings were often tempered by the growing awareness that with age can come recognition that, indeed, we bring years of knowledge and experience that inform not only our journey, but also the journey of those around us. together the stories of these 39 women, I explored the ways in which this work can give voice and power to the sometimes ignored and undervalued late-entry women in academia. This study is meant to contribute to the growing body of research on non-traditional students entering university. It concludes with implications for how our universities might consider that their policies regarding admissions, grant applications and funding opportunities could better serve the varied needs of late-entry women.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.024 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".