Bibliographic record
Abstract
The paper argues that although writings on social exclusion have largely ignored “lesbigays” (lesbians, gay men, bisexuals and transgendered people), they can be socially excluded. It begins by contrasting the cultural acceptance of lesbigay images with the reality of discrimination and homophobia. It then gives instances of this discrimination, such as criminal attacks, harassment and legal discrimination, including “Clause 28”. The next section of the paper looks at developments in the US and Canada and emphasises progress made within the US library profession. The paper then turns to UK public library services, referring to past research, including the (presently unpublished) Burning Issues Group survey of public library provision in London. It also comments on the general lack of research on lesbigays and public libraries. The effect of “Clause 28” is considered. The paper also suggests that libraries could learn from developments in the field of museums. It is concluded that overall provision for lesbigays is still patchy, with little attention having been paid to the needs of lesbigay communities. Recommendations for action are made (April, 1999).
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 | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".