Women, Transgender, Femme and Nonconforming People
Bibliographic record
Abstract
The purpose of this research is to explore the barriers and incentives that exist for women, transgender, femme and gender nonconforming people to participate in nighttime events on UBC campus. Through our research, we hoped to be able to understand in what spaces women, transgender, femme and gender nonconforming people want to see more events taking place, what would compel them to attend nighttime events on campus, and what is currently preventing them for participating in existing nighttime events. To answer these questions, we used a mixed-method qualitative approach. We started with a review of key literature on the subjects of gender, nightlife and inclusive spaces. This helped us understand what other Canadian campuses are doing to create inclusive campus events and also to frame our research through an intersectional feminist lens. We then collected our data through three outreach and engagement activities. We set up a booth at the NEST Building in central UBC campus with two large engagement boards, asking “where do you want to see a nighttime event on campus?” and “what would draw you to a nighttime event on campus?”. We approached groups of people passing by, and asked them to participate. Participants were asked to write their identifiers on sticky notes and place them on the two boards. If respondents were willing to participate further, we would conduct a short and informal interview that allowed us to gather more detailed information about barriers and incentives to participating in nighttime events on campus. Our last engagement method was in the form of a short online survey that we sent to three groups on campus that work to promote gender-diversity and inclusion at UBC. In our research we found that just over 50% of respondents wanted to see nighttime events in and around the NEST. The next most-desired location was in the Arts and Culture District of UBC campus. For type of events, those involving food, music, and drinking were most popular among our studied population. Accessible transportation has been identified as a main draw to nighttime events, specifically for those living off-campus, making up 60% of respondents. We found that many people attend events for social interaction or because their friends are also attending. Music is also a main draw to nighttime events; however, it can also be a deterrent to individuals if it they do not enjoy the type of music being played. Further, transportation, along with distance from home are the primary factors preventing our targeted population from attending nighttime events. Cost, food and busy schedules were also identified as being important factors. These findings have led us to make four key recommendations. First, event information should be shared and contained in a central location. Second, events should be free as often as possible. Third, events should be held in central locations, close to main transportation stops on campus and fourth, events on campus should have a greater diversity of content to attract a wider range of students. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".