Family, friends, and faith-communities: Intellectual community and the benefits of unofficial networks for marginalized scientists.
Bibliographic record
Abstract
Throughout the 20th century, female scientists faced barriers to participation in scientific communities. Within psychology, the 1st generation of women fought for inclusion in the university and access to laboratories; the 2nd generation officially gained access to such resources while still in practice being excluded from many areas of psychology and being denied suitable professional opportunities (Johnston & Johnson, 2008; Scarborough & Furumoto, 1987). Scholarship on these challenges tends to focus on power dynamics or on the strategies used by women to overcome obstacles to their full acceptance in the scientific world. In other words, there has been a focus on women's participation in official intellectual communities. Less attention has been paid to the motivational consequences of belonging to unofficial or informal intellectual communities. In this article, we argue that exploring the nature of unofficial communities illuminates a pattern of strategies that accounts for women's success in official communities; challenges a masculine, laboratory-centric model of science; and offers a model of intellectual work that has applications for other disenfranchised groups both in the history of science and in the modern world. We focus on 3 psychologists, Milicent Shinn, Eleanor Gibson, and Magda Arnold, whose success was underpinned by the support of unofficial networks. By so doing, we show how unofficial communities address specific needs for the marginalized. Finally, we explore applications to address the problems of the neoliberal university. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".