Networks of Support: Investigating a Counterspace that Provides Identity Resources for Minoritized Students in Post-Secondary Physics
Bibliographic record
Abstract
There is recent evidence suggesting that formal and informal support networks for minoritized students in science, technology, engineering, and mathematics (STEM) can contribute to their persistence in the field. These counterspaces can serve as “safe spaces” where deficit notions of minoritized students can be challenged and where a positive collegiate climate can be established. Generally, counterspaces are found outside of STEM departments: with mentors, in campus student groups, or in conferences focused on diversity in STEM. However, two participants in a study by Johnson and colleagues reported evidence suggesting that physics departments themselves can act as counterspaces. We are inspired by this finding to investigate the role that a network of support for minoritized students in a physics department of an Eastern Canadian university has on female students’ experiences in their physics program. In this paper, we present data suggesting that participation in this network can promote forms of participation that may be especially beneficial for minoritized students in physics. We argue that this network acts as a counterspace built into the structure of a university physics department. Furthermore, we suggest that access to counterspaces such as this one may be able to provide identity resources that minoritized students draw on to position themselves as insiders when entering physics programs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".