Bibliographic record
Abstract
I am very grateful to Barbara Brickman, the guest editor of this Special Issue of Girlhood Studies: An Interdisciplinary Journal for her term “dislodging girlhood” in the context of heteronormativity. Repeatedly in this issue Marnina Gonick’s pivotal question, “Are queer girls, girls?” (2006: 122) is cited. In the 13 years since she posed this question, we have not seen enough attempts made to address it. To mix my metaphors I see this issue of Girlhood Studies as helping to break the silence and simultaneously to open the floodgates to a ground-breaking collection of responses to Gonick’s question. Given the rise of the right in the US and in so many other countries, queer girls— trans, lesbian, gender non-conforming, non-binary to mention just a few possibilities—are at even greater risk than before. Girlhood Studies has always been concerned with social justice, so this special issue is a particularly important one in our history. It is also worth noting that many of the articles are written or co-authored by new scholars, signaling an encouraging trend in academic work that has social justice at its core. I thank Barbara Brickman, the authors, and the reviewers for their history-making contributions to the radical act of dislodging girlhood.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.099 | 0.065 |
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".