Half-Asian? Half-Valid?: An Autoethnographic Account of the Situational Mixed-race Experience
Bibliographic record
Abstract
This autoethnographic study uses the researcher’s personal racialized experiences to illuminate the complexities of being mixed-race. Understanding one’s own identity is crucial to positioning oneself in the world and experiencing one’s surroundings. For mixed-race individuals, understanding oneself becomes more difficult and nuanced as compared to monoracial groups. The mixed experience is marked with struggles with racial ambiguity, rejection from racial communities, and racial performativity. Feelings, including invalidation, self-doubt, discrimination, and longing for community often arise, prompting an investigation as to what it feels like to carry a mixed-race identity. This study contributes to the field of race and identity studies, exploring mixed-race identity from a first-hand perspective. Through three main frames of analysis: 1) perception of mixed-race by others, 2) internalization of invalidity, and 3) understanding the contextuality of the mixed identity, this paper delves into how identity is constructed uniquely for mixed-race individuals. Findings from this paper provide insight to the situational experience of mixed-race individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".