Within and Between Two Worlds: Conceiving, Measuring, and Applying Mixed-Ethnic Identity in Three Countries
Bibliographic record
Abstract
Accompanying the rising ethnic diversity of Western countries is a burgeoning number of mixed-ethnic unions and people with mixed-ethnic ancestry. These people do not fit neatly into one group or another. This ambiguity is compounded by the fact that their ethnic identity is affected by how they are perceived and labeled by others. Theories have been advanced to explain ethnic identity, and its corollaries for cognition, emotions, and consumer behaviors. However, aside from a handful of ethnographic studies, knowledge about how social identity of mixed-ethnic consumers is formed and shaped, and how it potentially affects consumer dispositions, remains largely uncharted. Using data gathered in three countries (Canada, United States, United Kingdom), and considering various ethnic mixture combinations, this article presents the development and validation of a multidimensional scale for measuring mixed-ethnic identity (MEI) and examines the relationships of the 13 MEI components to consumer dispositions commonly used to segment domestic and international markets. The consistency of the relationships between the MEI components and the established consumer dispositions are scrutinized. Implications for theory and practice are discussed.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".