Bibliographic record
Abstract
This thesis explores how female charitable-sector leaders draw on dominant discourses and how they counter them in their talk about leadership and learning. Drawing on rich narratives coming out of female leaders’ interviews, I demonstrate how gender and identities are constructed through dominant discourses drawing on feminist post-structuralism and intersectionality theory. This inquiry uses a transdisciplinary discursive approach combining Critical Discourse analysis (CDA), Feminist Post-structuralist Discourse Analysis (FPDA), and Discursive Psychology (DP). Drawing on these approaches ensures that gender remains at the forefront, that a connection occurs between societal discourses and day-to-day talk, and that the research attends to how subjectivities are created through talk. The intention is fourfold. First, this research aims for a greater understanding of how present masculine constructions of leadership are manifest in charitable organizational leaders’ talk. Second, it explores how such masculine leadership ideals are negotiated and challenged. It identifies how these discourses occur and create points of tension or ideological dilemmas. Third, it investigates moments where dominant discourses are contested in talk. Finally, this research considers how learning is implicated in these processes. The findings demonstrate, first, that women are positioned through dominant discourses of leadership, gender, and difference, and that this places them as something “other” than a leader. Dominant discourses, though they circulate broadly, penetrate the non-profit sector contextually. Second, the findings establish that charitable-sector leaders negotiate and challenge dominant discourses. Third, the findings demonstrate that women contest these notions through discursive mechanisms, including naming dominant discourses, using non-damaging discourses, and rediscursivization. The contestation of dominant discourses also occurs contextually and, sometimes, in contradictory ways and works to challenge the status quo. Fourth, learning is embedded in discourse resulting in women learning in and through dominant discourses as they lead. This research contributes to the understanding of how dominant masculine rationality is learned and perpetuated in leaders’ talk, as well as how it is challenged. This, in turn, provides new methodological tools and discursive approaches to research and the practice of social change work not only in the non-profit sector but also in leadership learning and social policy development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.048 | 0.010 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".