Book Review of: Garrison, Gary. (2018). Raising Grandkids: Inside Skipped Generation Families. Regina: University of Regina Press.
Bibliographic record
Abstract
Gary Garrison's book, "Raising Grandkids: Inside Skipped Generation Families", is an eyeopening, uplifting text that explores the issues of unsupportive child welfare systems, particularly in the domain of parenting in skipped-generation families.This book aims to acknowledge the many situations and difficulties of grandparents who are caregivers for their grandchildren and greatgrandchildren, whilst offering these caregivers a voice, practical help, and ultimately a community who understands.Nevertheless, this book also identifies "the challenges, suffering, joys, sacrifices, and successes [these caregivers] experience in this retirement activity called grandparenting," to uplift and affirm the unique advantages of skipped-generation parenting (p.xii).Garrison initially explores these topics surrounding grandparent-headed households through personal observations.He illustrates his own experience of raising his step-grandchildren and his perspectives on the relationship between support systems and skipped-generation families such as his own.Garrison then sets out to broaden the understanding of this phenomenon by interviewing grandparent caregivers in similar kinship situations.Across his own personal observations and the interviews (oral histories), many similarities arise which allows Garrison to form a supportive educational thread for this caregiver community.Garrison identifies that there are challenges faced by skippedgeneration kinships which are unrelieved by support systems and that social fear ultimately reduces these caregivers' ability to speak up about these struggles.The qualitative interviews that Garrison conducts with other grandparents in similar kinship situations bring about three central themes to
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.091 |
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".