Removing Barriers: Women in Academic Science, Technology, Engineering and Mathematics , and: Women, Gender, and Technology , and: Science and Social Inequality: Feminist and Postcolonial Issues (review)
Bibliographic record
Abstract
Reviewed by: Removing Barriers: Women in Academic Science, Technology, Engineering and Mathematics, and: Women, Gender, and Technology, and: Science and Social Inequality: Feminist and Postcolonial Issues Mary A. Armstrong (bio) Removing Barriers: Women in Academic Science, Technology, Engineering and Mathematics, Jill M. BystydzienskiSharon R. Bird, eds. Bloomington and Indianapolis: Indiana University Press, 2006, 347 pp., $75 hardcover, $30 paper. Women, Gender, and Technology, Mary Frank FoxDeborah G. JohnsonSue V. Rosser eds. Urbana and Chicago: University of Illinois Press, 2006, 204 pp., $55 hardcover, $20 paper. Science and Social Inequality: Feminist and Postcolonial Issues by Sandra Harding. Urbana and Chicago: University of Illinois Press, 2006, 205 pp., $40 hardcover, $20 paper. Interrogating how gender, race, sexuality, and transnational issues complexly intersect with science, technology, engineering, and mathematics (STEM) is not a new project for feminists. Yet each of the recent works reviewed here offer productive, interdisciplinary additions to the intricate landscape of these intersections, presenting valuable perspectives on the mutually transformative links between gender-based inquiry and STEM issues that lie at the heart of feminist science studies. Jill M. Bystydzienski and Sharon R. Bird’s Removing Barriers: Women in Academic Science, Technology, Engineering and Mathematics is a particularly useful and comprehensive collection that examines the persistence and seeming intractability of the under-representation of women in academic STEM areas. What makes this collection especially effective is the careful and convincing theoretical perspective by which it is informed. At the very center of Bystydzienski and Bird’s approach is the quite explicit rejection of more traditional approaches to understanding and “fixing” the problem of the underrepresentation of women in academic STEM areas. Specifically, the authors refuse to accept what they describe as “interventions that construe women as ‘the problem’ in need of change” and which [End Page 221] primarily focus on helping individual women adjust to doing science or acquiring skills they appear to lack (4). Similarly, the editors challenge the simplicity of the popular “pipeline” theory, noting that while the image of women progressively falling away from STEM careers is an apt one, the leaky pipeline model also fails to critique adequately the deeply masculinist cultural and structural barriers that are fundamentally embedded in science and engineering fields. This clear-headed approach to the problems of women and STEM success/retention allows the seventeen essays in this collection to grapple effectively with multifaceted levels of inquiry and analysis while avoiding any of the randomness or disjuncture that often plague such distinctly ambitious projects. Bystydzienski and Bird divide the work into four sections: historical issues concerning women in STEM, institutional and cultural barriers, feminist science studies, and ideas for remedies and change. The first section features essays by Sally Gregory Kohlstedt, who analyzes historical patterns concerning gender, science, and technology in the twentieth-century United States, and Amy Sue Bix, who specifically addresses the gendered history of engineering (chapters one and two, respectively). These two essays provide a valuable framework for the work that follows—work that often returns to the historical frame the better to explain the persistent exclusion of women in STEM fields. Section two foregrounds issues of race and the particular barriers faced by women of color. This section features Sally Hanson’s study of issues faced by African American women in science fields (chapter six) and Josephine Beoku-Betts’ discussion of issues encountered by African women who travel to “the West” (specifically the United States, Canada or Europe) to continue or complete STEM graduate work (chapter seven). Cogent analyses of the configurations and stubborn tenacity of cultural and structural barriers—lack of practical and abstract support, effective and ineffective pedagogical approaches, overt and covert discrimination, constricted access to resources, and limited opportunities for collaboration in research and grant-writing—make this section relevant to all feminist educators attempting to address the under-representation of women of color in STEM fields. Especially useful in this context is Sue V. Rosser’s “Using POWRE to ADVANCE: Institutional Barriers Identified by Women Scientists and Engineers” (chapter three) which usefully outlays the specific obstacles most frequently faced by women in STEM. Molly J. Dingle’s chapter on the effects of the gendered atmosphere of the college science classroom and its subsequent...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".