Assembling The Digital Girl/girl: Making Meaning Through Social Media
Bibliographic record
Abstract
This dissertation explores the digital becoming of girls through the various ways in which they are (re)made on social media. By using the thoughts and experiences of real girls, we explore together how we “make the Girl/girl mean” in our collective North American culture and society. Through interviews and focus groups with 23 girls located in the Greater Toronto Area, I developed six themes that outlines how the Girl (hegemonic discourses) is currently defined. Throughout my exploration of these themes, I critically analyze these definitions through an extensive review of girlhood, feminist, and social science literature. I bring into conversation previous research and theory with the emerging knowledge produced by the girls in this study and myself. My research creates a context specific roadmap, or what we might call an “assemblage” of the Girl as she exists in this current moment. Further, I think through how these Girl/girlhood subjectivities and discourses work in service to oppressive systems. I then think critically about what the Girl means to the girls in my study and real girls in general. In thinking through the lived, material realities of girls, I offer recommendations that can help us chart paths for the future, in which girls can be supported to safely exist in and explore this one wild and precious life.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.029 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".