Feminist theorizing in organization studies: A way forward with Marta Calás and Linda Smircich
Bibliographic record
Abstract
The founders of Organization include Marta Calás and Linda Smircich who are among the most influential feminist theorists in organization studies. We take inspiration from their work to outline ideas for feminist and other critical scholars studying organizations and organizing. We draw especially on their consistent interest in transnational feminism, engagement with feminist new materialisms, and emphasis on epistemological and ontological questions about (feminist) organization studies. We highlight key theoretical points and show how feminism(s) can remain socially, societally, and globally meaningful. Our aim is to continue to create feminist organization theorizing that, as Calás and Smircich’s scholarship does, remains critical and vigilant about who its knowers are, what kind of knowledge it produces, and what this knowledge is for.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.022 |
| 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".