Using Focus Groups to Explore the Underrepresentation of Female-Identified Undergraduate Students in Philosophy
Bibliographic record
Abstract
This paper is part of a larger project designed to examine and ameliorate the underrepresentation of female-identified students in the philosophy department at Elon University. The larger project involved a variety of research methods, including statistical analysis of extant registration and grade distribution data from our department as well as the administration of multiple surveys. Here, we provide a description and analysis of one aspect of our research: focus groups. We ran three focus groups of female-identified undergraduate students: one group consisted of students who had taken more than one philosophy class, one consisted of students who had taken only one philosophy class, and one consisted of students who had taken no philosophy classes. After analyzing the results of the focus groups, we find evidence that: (1) one philosophy class alone did not cultivate a growth mindset among female-identified students of philosophy, (2) professors have the potential to ameliorate (or reinforce) students’ (mis)perceptions of philosophy; and (3) students who have not taken philosophy are likely to see their manner of thinking as being at odds with that required by philosophy. We conclude by articulating a series of questions worthy of further study.
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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 | 0.068 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".