Interrogating the Links between Complex Social Identity Structures, Communicative Factors, and Psycho-Social Wellbeing in Multiethnic-Racial Populations in the United States and Canada
Bibliographic record
Abstract
Multiethnic and multiracial populations are among the fastest growing demographic groups in North America. Despite this fact, and a societal shift toward discussing race as a social construction, biological lay theories of race remain pervasive. Thus, those that are members of more than one ethnic-racial group face unique liminal identity experiences that monoethnic-racial individuals do not. In this dissertation I aim to explore these unique identity experiences, as well as their links to communication and wellbeing, through a lens of social identity complexity theorizing. In chapter one, I offer a brief history of miscegenation and amalgamation in North America to situate the study in the current sociocultural context. Following this, I review and synthesize research and theorizing on social identity complexity, multiethnic-racial identity, and the communicative factors that may affect the relationship between multiethnic-racial identity complexity and wellbeing. In chapter two I outline the methods, including procedures, measures, and analyses, which were used to answer my research questions. This dissertation employed a multimethod survey design including open-ended questions and scale items. In chapter three, I report the findings of the phronetic iterative qualitative analysis wherein I found five themes and seven subthemes surrounding multiethnic-racial individuals’ experiences having multiple ethnic-racial ingroup memberships. In chapter four I report the results of the quantitative analyses which estimated a number of multiple regression models to assess the relationships between multiethnic-racial identity complexity and wellbeing. Additionally, I estimated several moderation models to assess how the relationships between participants’ multiethnic-racial identity complexity and wellbeing was moderated by communicative factors. Finally, in chapter four I discuss the integration and interpretation of the qualitative and quantitative findings, outline major implications of the study, and report on limitations and directions for future research.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".