A call for conscious “anti-preparation” to motherhood
Bibliographic record
Abstract
Our privileged position as doctoral students and mothers allows for a critical look at this issue that leads us to propose a form of “anti-preparation” to motherhood, in the sense that women should be called upon to deconstruct the constraining beliefs surrounding motherhood rather than accumulate new knowledge that is sometimes contradictory, often useless, and particularly anxiety-provoking. This is not a call to ignorance, but it does encourage us to temper our consumption of knowledge that can generate beliefs that erode and, above all, invisibilize women’s intrinsic strengths. While some of information surrounding motherhood is essential to know, we suggest that more attention must be devoted to empowering mothers and challenging dominant norms that ultimately don’t always serve them. In short, this plea seeks to challenge the authoritarian function of medicalized knowledge embedded in capitalist and patriarchal norms of motherhood, which work to minimize the intrinsic strengths of mothers. Our intention is not to promote an inclusive view that rests on the commercialization of motherhood, but rather an anti-oppressive approach that legitimizes both the intrinsic strengths of mothers and their facilitating beliefs, whether medicalized or tacit.
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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.073 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".