Monosexism and bisexual identity disclosure in the online dating environment

This thesis examines the role of internalised monosexism on the formation of positive bisexual identification and subsequent disclosure decisions. While much of the research on sexual identity has focused on disclosure outcomes, little research has focused on this in relation to bisexual identity, particularly in the context of online relationship formation. This thesis applies social identity theory to bisexual identity to produce a model that predicts the disclosure of bisexual status to potential romantic partners on Tinder and more generally. The model is tested by means of an experimental design (n = 107), in which participants in the experimental condition (n = 51) are asked to challenge monosexist ideology as a method of social change to see its effect on internalised monosexism, bisexual identity, and subsequent disclosure decisions. Results demonstrate that, while the experimental manipulation was unsuccessful, internalised monosexism was present at low levels in the sample and was a significant predictor of positive bisexual identity and disclosure. These results also point to the importance of distinguishing negative from positive aspects of bisexual identity, as the relationship between internalised monosexism and disclosure was more strongly mediated by negative identity than it was positive identity. This thesis concludes with a discussion of the limitations of the study in relation to the unsuccessful manipulation of internalised monosexism, the use of social identity theory for explaining bisexual identity and identity-related outcomes, and also argues that future research may seek to identify other methods for bisexual people to achieve positive identification in the form of collective action

Being There: Young People Supporting Their Friends through Tough Times

This report documents one of the largest mixed methods studies to date examining informal help support provided by young people to their friends. We report on national survey data (n=169), as well as focus group data with 34 young people aged 16 - 25 who provide support to their friends. Specifically, the study examines the experiences of friends who support friends through tough times by focusing on how they perform this support, what resources young supporters use and have access to, what constrains this support and what they might need to enable the support they provide to their friends and peers. Our findings show the critical work young people are doing as supporters and documents the careful personalised support they offer their friends. Following sector consultations and discussions with young people, we offer key recommendations to ensure young people are resourced and supported in their care practices

Integrated root phenotypes for improved rice performance under low nitrogen availability

Greater nitrogen efficiency would substantially reduce the economic, energy and environmental costs of rice production. We hypothesized that synergistic balancing of the costs and benefits for soil exploration among root architectural phenes is beneficial under suboptimal nitrogen availability. An enhanced implementation of the functional-structural model OpenSimRoot for rice integrated with the ORYZA_v3 crop model was used to evaluate the utility of combinations of root architectural phenes, namely nodal root angle, the proportion of smaller diameter nodal roots, nodal root number; and L-type and S-type lateral branching densities, for plant growth under low nitrogen. Multiple integrated root phenotypes were identified with greater shoot biomass under low nitrogen than the reference cultivar IR64. The superiority of these phenotypes was due to synergism among root phenes rather than the expected additive effects of phene states. Representative optimal phenotypes were predicted to have up to 80% greater grain yield with low N supply in the rainfed dry direct-seeded agroecosystem over future weather conditions, compared to IR64. These phenotypes merit consideration as root ideotypes for breeding rice cultivars with improved yield under rainfed dry direct-seeded conditions with limited nitrogen availability. The importance of phene synergism for the performance of integrated phenotypes has implications for crop breeding.Peer reviewe

Fine-tuning language models to find agreement among humans with diverse preferences

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another