I Didn’t Know it was a Thing Either: Women Engineers’ Experience of Suffering in the Workplace
Bibliographic record
Abstract
This Canadian study investigates women engineers’ lived experiences of suffering in the workplace and aims to contribute to addressing the persistent problem of attracting and retaining women in engineering. It is inspired by my own experience of suffering in the engineering workplace, which I inquire into deeply as part of this thesis in an autoethnographic study. The autoethnography plays an essential role of critical self-reflection in service of this thesis’ primary research. My research on women engineers’ experience of suffering in the workplace uses a phenomenological, reflective lifeworld approach (Dahlberg et al., 2008). This approach is not widely used in organizational studies because, I argue, it represents a radical paradigm shift that is not easily understood. I endeavor in this thesis to make it more accessible and illuminate its potential to create disruptive, productive knowledge. \nFor my primary research, I use a purposeful sampling procedure to identify six women engineers who, together, represent a rich variation of experiences of the phenomenon. Each participant provides a critical situation narrative in which they are asked to write a direct, personal account of “a meaningful and vivid memory of an incident in the workplace that contributed to your suffering.” They then participate in two in-depth conversational interviews where their experiences of severe and protracted distress are explored. Analysis of women engineers’ intimate, personal experiences of suffering in the workplace found that some women engineers who suffer are trapped in an oppressive, socially constructed reality in which they protect themselves from threats and sacrifice their dignity, self-worth, health, well-being, and job effectiveness. Analysis of my findings against existing research on women in engineering and microaggressions results in six provocative insights. Systemic interventions that acknowledge and address the inequality in engineering are proposed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".