Book Review of Eckler, Rebecca. (2019). Blissfully Blended Bullshit: The Uncomfortable Truth of Blending Families. Toronto: Dundurn Press.
Bibliographic record
Abstract
s book, "Blissfully Blended Bullshit," discusses personal stories about her experiences with blending families with her now ex-boyfriend.Throughout this book, Eckler reveals the uncomfortable truths about blending families, which "so-called-experts" seem to leave out.In this book, Eckler and her daughter Rowan move in with her boyfriend, who also has two biological daughters of his own, and they end up having a son together, Holt.Eckler and her boyfriend were blindly in love and rushed to move in together, hoping their lives would adjust and become more normal.A relationship, which started as a one-night stand, quickly formed into a blended family with no prior preparation for what was about to come.Eckler stresses common themes throughout the book, such as relationship planning/setting guidelines, the uncomfortable emotions and stressors which arise in blending families, and how different blended families are compared to nuclear families.Eckler and her ex-boyfriend did not plan how their families would blend or set guidelines which describe what each partner expects from the other throughout the relationship.This may have been the seed that caused their relationship's downfall.She admits that "there was no sitting down and talking about the logistics, no discussion of how it would affect the kids, no dialogue
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.140 | 0.094 |
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".