Bibliographic record
Abstract
Supporting the development of children’s autonomy is widely recognized as a fundamental good. Despite this, social practices that reflect and reinforce patriarchal gender norms are ubiquitous. These norms, however, curtail the development and exercise of children’s autonomy by constraining their opportunities along gendered lines and promoting falsehoods about “natural” identity expression. In recognition of these limitations, many parents across the political spectrum are pushing back against patriarchal gender norms in their parenting approaches. One particular model that aims to fully embrace a progressive parenting methodology is the “gender-open” model of parenting (GOP). Broadly, the GOP methodology involves withholding disclosure of a child’s biological sex as assigned at birth from public knowledge. With the necessary support, this approach aims to encourage children to choose their own gender in their own time. Advocates of this model claim that adopting the GOP ensures a child’s autonomy in self-expression. But despite these claims, none of the advocates clearly articulates how the model promotes children’s autonomy. My aim in this paper is to demonstrate how the GOP protects and promotes children’s autonomy in robust ways, making a strong case for its adoption.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".