Bibliographic record
Abstract
"To be a young mother is almost by definition to be considered an "unfit" mother. Thus, it is not surprising that young Canadian, U.S. and Australian mothers are often scorned, stigmatized and monitored. This is a book about being young, being a mother, and grappling with what it means to inhabit these two complex social positions. This book critiques the dominant, negative construction of young motherhood. Contributors reject the notion that the "ideal" mother is a 30ish, white, middle-class, able-bodied, married, heterosexual woman situated in a nuclear family. This collection privileges the insights and stories of a diverse array of young mothers such as; a young mother coerced into giving her child up for a adoption, a young queer mother who has been parenting a child borne by her trans partner and who is now pregnant herself and many more. The tales analyzed and recounted in the collection record experiences of pain and joy, frustration and success, struggle and resistance, oppression and empowerment. We invite readers to hear the all too often silenced stories of young mothers, to learn what prevents and what allows these mothers to lead lives of grit, determination, authenticity, and agency as they strive to lovingly care for themselves, their children, and in many cases, other young mothers."--
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".