Handle with Hair: A Qualitative Course-Based Inquiry into How CYC Students Think About the Relationship between Hair, Identity, and Self-Perception
Bibliographic record
Abstract
This course-based research study explored CYC students’ thinking about the relationship between hair, self-perception, and identity. It focuses on CYC students for two reasons. First, the ability to form meaningful relationships with youth of many different cultural backgrounds and diverse lifestyles is an essential skill of CYC practitioners. Second, CYC students are encouraged to engage in discovery learning aimed at uncovering their unrecognized assumptions, cultural biases, attitudes, assumptions, stereotypes, prejudices, and privileges to ensure respect for the dignity of every person, regardless of their unique characteristics. Data was collected through online interviews and an arts-based activity. From the data analysis, the following four main themes were extracted: a) the power of hair as a symbol of beauty, b) the relationship between hair and self-esteem, c) hair oppression is real, and d) changing hair styles and life transitions. The findings of this course-based study support the existing literature on the significance of hair as a signifier of culture, identity, resistance, and social inclusion.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".