Book Review of Eckler, Rebecca. (2019). Blissfully Blended Bullshit: The Uncomfortable Truth of Blending Families. Toronto: Dundurn Press.
Bibliographic record
Abstract
Rebecca Eckler's book "Blissfully Blended Bullshit" is a lightheartedly put together personal journey through her experience with blending families and the ramifications of children, partners, and exes involved in her new life.Through anecdotes of her day to day, Eckler walks us through the conflicts of priority, unconscious bias, and jealousy that slowly buds and takes to evolve before the blending even begins.She also includes outsider conversations of friends who have been or are in blended families, and the daunting perspective of in-laws, all who seem to be in unanimous agreement: blended families are not as homogeneous as we are led to believe.The well known phrase "blood is thicker than water," which is meant to emphasize the importance of blood relations over friendships, is a common saying in many household circles, and Eckler finds this still holding true when it comes to blending families.As she eloquently puts, "many say you need to make your partner a priority when you are in a blended family.Other "experts" say that kids should always be the priority.So which fucking is it?"(p.209).Through a journey of introspection, Eckler navigates the parallels between her need to prioritize her only
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.059 |
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".