Voting Behaviour among the Gay, Lesbian, Bisexual and Transgendered Electorate
Bibliographic record
Abstract
Abstract.The gay, lesbian, bisexual and transgendered (GLBT) population is a good example of a demographic group that has been understudied because it is difficult to develop a subsample of sufficient size from typical national samples. Here we exploit the extraordinary size of a 2006 online election day survey (with about 35,000 respondents) to examine how the GLBT community behaves politically. While it will surprise no one that this community bestowed little support on Stephen Harper's Conservative party in the 2006 federal election, the factors behind such a consistent vote pattern are not adequately understood. In order to shed more light on the voting behaviour of the GLBT electorate, we develop a socio-demographic profile of the group, and explore three explanatory angles: 1) salience of issue campaign dynamics, given that the same-sex marriage issue was prominent in 2006; 2) ideological and attitudinal proclivities; and 3) strategic considerations. Résumé.La population gaie, lesbiennes, bisexuels et transgenres (GLBT) est un exemple d'un groupe démographique qui a été peu étudié, car il est difficile de développer un sous-échantillon de taille suffisante à partir d'échantillons nationaux. Ici, nous exploitons la taille extraordinaire d'une enquête enligne du jour du scrutin fédérale du 2006 (avec environ 35.000 répondants) d'examiner comment la communauté GLBT se comporte politiquement. Bien qu'il ne surprendra personne que cette communauté accordé peu d'appui sur Parti conservateur de Stephen Harper lors de l'élection fédérale de 2006, les facteurs qui expliquent un tel motif ne sont pas bien compris. Afin de jeter plus de lumière sur le comportement de vote de l'électorat GLBT, nous développons un profil sociodémographique de cette groupe, et d'explorer trois angles explicatives: 1) pertinence de la question du mariage de même sexe, 2) tendances idéologiques, et 3) des considérations stratégiques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".