What is Needed to Create Gender Inclusive Learning Organizations?
Bibliographic record
Abstract
This chapter uses a critical feminist lens to explore Senge’s model of the learning organization as an example of Weber’s ideal-type theoretical framework, focusing on the five disciplines outlined by Senge (personal mastery, team learning, shared vision, mental models, and systems thinking). It explores the concept of learning organizations in a variety of gendered workplace contexts, including universities, fire services, and militaries to consider how Senge’s model could be used to illuminate the multiple barriers that women face in these contexts. Drawing upon critical feminist scholarship, the authors note that a patriarchal conception of rationality, which is often the bedrock of many organizational structures, needs to be challenged in order for gender equity to be realized. They conclude by using the example of a not-for-profit organization that supports women mystery writers, Sisters-in-Crime, to explore how an emphasis on addressing equity for women may create gender inclusive learning organizations.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".