LGBTQ2S+ and Community Belonging in Rural Perth-Huron: Community Services for the Needs and Experiences of LGBTQ2S+ Community Members
Bibliographic record
Abstract
Rural-specific experiences of LGBTQ2S+ individuals are often overlooked, and data regarding this unique population is lacking. Current data suggests increased levels of discrimination experienced by LGBTQ2S+ populations in rural areas, in comparison to those in urban areas. There is a call to action for rural-specific LGBTQ2S+ community services as well as multi-levelled strategies in order to decrease discrimination and thus increase community integration. This research seeks to fill the data gap of the experiences and needs of LGBTQ2S+ community members from the rural municipalities of Perth-Huron, with attention to both individual levels of discrimination and systemic levels of discrimination. This data will provide a better understanding to service providers and create effective context-specific programs and services. Further, this data will increase community collaborations in order to achieve more just and equitable communities, and thus the realization of a more neighborly community. In order to ensure the data reflects the experiences and needs of the LGBTQ2S+ community members specific to Perth-Huron, I will implement both quantitative and qualitative methodologies. Data collection will include needs assessment surveys, focus groups, and interviews. As well, data mobilization through the sharing of analyzed results with collaboration partners and a publication of a final report will provide recommended actions to community service providers. This project will allow service providers to effectively target their efforts in order to better support the LGBTQ2S+ community. This will create a better sense of belonging and reduced discrimination for rural LGBTQ2S+ people within the broader communities of Perth-Huron.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".