Book Review of: Jorgensen, L. (2015). First Gear: A Motorcycle Memoir
Bibliographic record
Abstract
tells of the author's childhood physical, emotional and sexual abuse.She tells the story as she rides on her motorcyclenamed Thelma D.from Ottawa to Winnipeg and back.During her ride she shares stories of her childhood growing up in the seventies with a violent father, alcoholic mother and her three brothers."First Gear: A Motorcycle Memoir" is conclusively a book of courage, survival and recovery from childhood trauma that went on into her adult life, taunting her with addiction, depression and anxiety.Jorgenson, now fifty years old, and living with Multiple Sclerosis, is an author that provides extraordinary amounts of honesty in her writing as well as unbelievable imagery.Jorgenson talks about her childhood trauma throughout this book.Rather than identifying 'Eric' as her father, she decides to use his name as he is the main one putting her through the abuse that she had endured.Her brothers were receivers of physical and emotional abuse from Eric, however, Jorgenson also received sexual abuse as well.It happened in her own home, and her mother Bev knew about it but did nothing to stop it because she was afraid of Eric and what he might do if she tried to take a stand.I believe that she chose to talk about her childhood abuse and how it affected her far into her adulthood because she hopes that telling her truth would be able to help others dealing with troubles of their own.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.197 | 0.137 |
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".