Bibliographic record
Abstract
Women in the U.S. and Canada pay a substantial social and economic penalty for becoming mothers. And though the existence of a “motherhood penalty” has been extensively demonstrated, motherhood itself has not been widely recognized as a marginalized identity. In this article, I review several popular visualizations (graphical representations, imagery, infographics, etc.) used to depict inequality and oppression to propose that—despite mothers paying a motherhood penalty—motherhood remains an invisible category in current representations of social inequity. I suggest that by subsuming mothers under the category of “women,” current visualizations obscure how gender discrimination (particularly economic discrimination) results from women’s status as “mothers” rather than their status as “women.” As a result, we miss the central role that motherhood plays in women’s social and economic oppression. Motherhood is rarely recognized as an identity that contributes to women’s inequality, and I argue here that this is partially due to its invisibility in popular visualizations of oppression. As a result, I argue that motherhood should appear as an analytic category in our popular visualization of oppression to increase its visibility as a marginalized identity. Such visibility would increase social justice activism around issues of motherhood and would raise public awareness of motherhood as a significant social identity within the context of oppression and inequality.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".