Dolls studies : the many meanings of girls' toys and play
Bibliographic record
Abstract
Robin Bernstein: Children's Books, Dolls, and the Performance of Race or, the Possibility of Children's Literature - Lisa Marcus: Dolling Up History: Fictions of Jewish American Girlhood - Alexandra Lloyd: Dolls and Play: Material Culture and Memories of Girlhood in Germany, 1933-1945 - Meghan Chandler/Diana Anselmo-Sequeira: The Dollification of Riot Grrrls: Self-Fashioning Alternative Identities - Jennifer Dawn Whitney: It's Barbie, Bitch: Re-reading the Doll Through Nicki Minaj and Harajuku Barbie - Vanessa Rutherford: Technologies of Gender and Girlhood: Doll Discourses in Ireland, 1801-1909 - Naghmeh Nouri Esfahani/Victoria Carrington: Rescripting, Modifying, and Mediating Artifacts: Bratz Dolls and Diasporic Iranian Girls in Australia - Elizabeth Chin: Barbie Sex Videos: Making Sense of Children's Media-Making - Juliette Peers: Adelaide Huret and the Nineteenth-Century French Fashion Doll: Constructing Dolls/Constructing the Modern - Catherine Driscoll: The Doll-Machine: Dolls, Modernism, Experience - Judy Shoaf: Girls' Day for Ume: Western Perceptions of the Hina Matsuri, 1874-1937 - Erich Fox Tree: The Secret Sex Lives of Native American Barbies, from the Mysteries of Motherhood, to the Magic of Colonialism - Amanda Murphyao/Anne Trepanier: Canadian Maplelea Girl Dolls: The Commodification of Difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.052 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".