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 biography of her experience of creating a blended family where her new boyfriend, along with his two daughters, moves into the home she currently lives in with her daughter Rowan.Eckler does not rely on statistics, nor any scientific data related to blended families to convey what life is like in such a situation.Instead, she relies on her experience to convey what could occur in such a relationship.The author's perspective, along with advice Eckler receives from friends, is used to convey how at the beginning, things may seem perfect in a blended family, but over time things are not exactly how they seem.The further they get into the relationship, the more she realizes that things are not as perfect as they seem.Her realization that love is not equal, along with issues involving finances, and finally realizing that people change when a relationship ends, and things are not going as well as they believed.The book is organized ideally to showcase the different points in their relationship and the effects that it has on her.Eckler describes that at the beginning of their relationship, they did not prepare any rules or financial responsibilities because they "are so in love, none of that seems to matter" (p.29).Everything felt right in the author's mind, and they had all spent time together, so the possible
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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) | 0.002 | 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) | 0.131 | 0.090 |
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".