Lesbian Neighbourhoods: The Disappearance and Displacement
Bibliographic record
Abstract
Gay neighbourhoods, defined by their high concentration of gay men and unique culture, have seemingly become a staple in major cities in the United States, Canada, and Europe. Their notion of a ‘safe space’ has allowed gay men to socialize and find partners, initiate successful political movements and the protection of gender and sexuality rights, and be their authentic selves (Ghaziani, 2015). Geographers and sexuality scholars have been researching the implications of physical space and the importance of occupying neighbourhoods for gay men, as their gentrification and consumerist culture has drawn significant attention (Bell & Binnie, 2004). However, scholars have overlooked that while gay men have been creating neighbourhoods, so too have lesbians, with their formation and sense of community impacted by heteronormative and patriarchal boundaries.\nThroughout creating a systematic review on gay neighbourhoods, I noticed a lack of attention to valuable discourses about where lesbians occupy, why they occupy certain neighbourhoods and institutions, and how the transformation of the gay neighbourhood impacts the future of lesbian neighbourhoods. Moreover, those articles included in the review are rooted in feminist scholarship and extend the discussion beyond what is relevant to the initial study, which is why I have created this short review to discuss the history of lesbian neighbourhoods, their slow disappearance, and urge researchers to consider why lesbians patterns of occupying are changing.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".