Through writing for publication, a biracial, bicultural, bilingual adolescent explores identity and normalcy: Sarah in her own words
Bibliographic record
Abstract
This research attempts to fill gaps in the literature concerned with language and identity of interracial, intercultural, bilingual individuals. First, the literature is dominated by black-white racial mixing in the U.S. context. Other racial mixes in other contexts receive scant attention. Second, perhaps because the literature is dominated by U.S.-based research, little attention is paid to language in the context of being interracial. Third, research has made little use of these individuals' own voices. Therefore, I invited mixed race individuals who grew up in Japan and Canada to contribute to a collection of writing about their experiences as interracial, intercultural, bilingual individuals growing up and going to school in Japan or Canada. This research attempts to apply activity theory in the analysis of the data of one writer, Sarah, to examine the relationship of the individual to the social structures or institutions that make up her worlds. As I understand and use activity theory, it provides a temporary and permeable boundary for the phenomena I wish to examine. Activity theory presents a way of identifying and tracing the interaction of individuals with social constructions, particularly through the flow of artifacts, in this case the drafts of Sarah's writing. Activity theory also has limitations. I hope to be able to identify the strengths and limitations by the end of this analysis. This research also attempts to break ground in research methodology by gathering the data through a writing-for-publication process. I engaged in a collaboration with twenty-three students as writers with the goal of producing publishable prose. The young people participated simultaneously as writers and research participants. I worked simultaneously as an editor and a researcher. Through the process of writing and editing individuals constructed the self they wish others to see and recognize. This thesis presents one of those writers, her writing and editing. The thesis is my attempt to show the complexity, dynamism, and ongoingness of that process of self construction rather than a reductive, static, consideration of the variables affecting language and identity development.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".