Bibliographic record
Abstract
This article examines the implications of two provocative scholarly slogans—Donna Haraway’s “Make Kin Not Babies” and Sophie Lewis’s “Abolish the Family”—for children, childhoods, and ecopedagogies in the early years. Engaging critically with these concepts, the article highlights their potential to disrupt entrenched norms while acknowledging the discomfort and uncertainty they may evoke. It begins by interrogating the normalized centrality of “the family” in early childhood teacher education and childhood studies, arguing that this focus constrains alternative imaginaries of care and justice. The discussion situates Haraway’s and Lewis’s slogans within broader debates, critiquing how idealized family structures often align with humanist stewardship pedagogy, which presumes humanity’s exceptional capacity to save both children and the planet. By contrast, speculative narratives, such as Netflix’s Sweet Tooth , offer opportunities to reimagine the child, family, and future by emphasizing interconnections between humans and the more-than-human world. These stories open space for counter-imaginaries, inspiring alternative communal formations and transformative practices. Ultimately, the article advocates for a pedagogy of discomfort and generous suspicion as ecopedagogical strategies to interrogate existing care structures and envision more equitable and interconnected futures.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.113 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".