Examining How Communication and Demographic Factors Relate to Attitudes Toward Legalization of Same-Sex Marriage in Singapore
Bibliographic record
Abstract
The controversial issue of same-sex marriage has sparked debates on political, social, moral, and religious fronts in many countries (Duggan, 2008). As of March 2013, 11 countries, including Canada and Denmark, as well as several regions in Brazil and the United States, have legally accepted same-sex marriages (Stritof & Stritof, 2013). However, same-sex marriage is illegal in most Asian countries, such as Japan and China (Koh, 2012). Similarly, same-sex marriages are not allowed in Singapore, under Section 377A of the Singapore Penal Code, which criminalizes sexual relations between adult men (Outrages on decency, 1985). The debate on same-sex marriages has been ongoing within the island-state (Koh, 2012; Lai, 2013). In 2011, the annual “Pink Dot” event in Singapore attracted over 10,000 people to advocate sexual freedom (Tan, 2011). Supporters of gay rights have called for a repeal of Section 377A in 2007 when the bill to amend sections of the Penal Code was debated in the Singapore parliament (Li, 2007). However, the majority of the Singaporean public hold negative attitudes toward lesbian and gay men (Detenber et al., 2007) and were against legalization of same-sex marriages in Singapore (Ho, Chen, & Sim, 2013). Furthermore, positive portrayals of lesbian and gay men lifestyles are discouraged in traditional media, in part, due to conservative public opinion and values (Radcliffe, 2008).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".