Been, being, becoming: an auto-ethnographical analysis of black youth in Canada
Bibliographic record
Abstract
Black youth often contend with negative external social constructions, labels, and categories, defining who they are as individuals and as racialized others. Regardless of the degree to which Black youth identify with these narratives of deviance, the expectations and assumptions within this discourse have consequences. This research project analyzed Black youth identity and racialization through the lens of my racialized experience of growing up Black in Canada. Thus, this study attempted to answer the following question: How have I as a Black youth made sense of the “narrative of deviance” as I created my identity during adolescence? The method used for this research was an auto-ethnographical approach, which allowed me to analyze my own life experiences and explore the themes in relation to academic literature on Black youth and adolescent experiences. As the primary researcher I coded the selected life experiences using MAXQDA coding software, analyzed them for major themes, and drew on the major connections that existed between the data and the existing literature. The existing literature represented Black youth identity as frequently being fraught with internal identity tension, varying levels of performative tendencies, and denial of individual recognition. My research found that throughout my life, I contended with social process that constructed Blackness, through creation, performance, and judgment, making my Blackness an object that was meant to represent a stereotypical image of a Black male.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".