Review of Sarah Marie Wiebe's <i>Hot Mess: Mothering Through a Code Red Climate Emergency</i>
Bibliographic record
Abstract
In Hot Mess: Mothering Through a Code Red Climate Emergency, Sarah Marie Wiebe delves into the important but often overlooked intersection of motherhood and the ongoing climate emergency. The book offers a thoughtful exploration of how new mothers experience the climate crisis and emphasizes the necessity of centring care in the discourse on environmental justice. Wiebe's main argument revolves around the need for pushing past individualism to truly centre care and community at the heart of climate crisis mitigation efforts. She draws from her own experiences as a new mother, as well as the experiences of other communities in Hawaii and rural settings across Canada, to illustrate the tangible impacts of climate change on maternal health and community well-being.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".