Bibliographic record
Abstract
In popular media and academic literature, 2SLGBTQ+ individuals who live in rural environments are often viewed as outcasts, unable to find an intimate partner, leading to living an unfulfilling life. This idea, portrayed by media, has always felt like a trop and not a real indication of how 2SLGBTQ+ individuals who live in rural environments are actually existing. Through my research I hope to understand the real impacts of 2SLGBTQ+ individuals who live in rural environments through understanding how their environment impacts different aspects of their lives. The areas I have researched include access to healthcare and community support while also dealing with the public perceptions of the 2SLGBTQ+ community. These struggles all impact each individual’s sense of self, body image and identity for themselves and how they as individuals present themselves to the world. One’s ability for a strong presentation of self to the world plays a pivotal role, if and when an individual will engage in an intimate relationship. For my research I looked at various research articles, studies and news articles from all over the world to help create a well-rounded picture as there is not just one story when it comes to rural 2SLGBTQ+ individuals. Through my research I found that although 2SLGBTQ+ individuals do face hardship in accessing supportive healthcare and 2SLGBTQ+ youth do experience bullying in their school environments, not everything is negative. There are significant numbers of the 2SLGBTQ+ adult population who live rurally by choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.249 | 0.048 |
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".