An investigation into gender diverse populations in hackathon environments
Bibliographic record
Abstract
This paper presents an investigation into whether there are any barriers to the participation of gender diverse student populations in engineering hackathons.There has been some research into the experience of women in hackathons, which has shown that hackathons can be alienating, or even hostile, towards under-represented groups in engineering.This paper is part of a larger research study to identify whether (or not) STEM environments are providing safe and inclusive spaces for people of all genders to encourage diversity and equity within this field.Pertinent to this paper, data collection was a pre-survey and post-survey over the course of two hackathons, one offered through a women-centered space and the other to the general student population.The Situational Motivation Scale (SIMS) survey instrument was given to participants of the 2 events; once at the start of each event, and once again near the end.The study participants were asked to generate a unique ID code so that their responses could be connected across the survey offerings.In total, approximately 70 students filled in the first survey, and 10 filled in the second across both hackathons.The results suggest that women participating in hackathons with the general student population may exhibit less intrinsic motivation than their male peers, but that events which are designed to be welcoming to gender-diverse participants can increase their intrinsic motivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".