Bibliographic record
Abstract
Where We Have to Go is a luminous and sassy first novel about the last days of childhood in a family coming apart at the seams. At once wryly humorous and deeply affecting, this sparkling novel follows the irresistible Lucy Bloom as she searches for her place in the world. When we first meet Lucy, she’s an imaginative eleven-year-old dreaming of a taste of freedom — and only beginning to grasp that all is not well between her parents. In the years that follow, Lucy’s journey to adulthood will see her question the limits of unconditional love, grow “criminally thin” as she stops eating, and discover complicated truths about what it means to be a young woman. Through it all, the central figure in Lucy’s life remains her mother, Joy, whose larger-than-life stories and boisterous voice belie a deep disappointment. As their relationship is tested again and again, Lucy comes to understand the resilience of the bonds that tie us to the ones we love. Among the characters we meet are Lucy’ s father, Frank, a failed glamour photographer turned travel agent who’s never been out of the country; her best friend, Erin, an artist whose outspoken iconoclasm will inspire and challenge Lucy; and Crashing Wave, Frank’s lover, a former exotic dancer and the woman Lucy comes to imagine as the ideal of all that is feminine. Set in Toronto throughout the 1990s, Where We Have to Go is a novel of self-discovery, family, and love. It introduces Lauren Kirshner as one of our most striking new voices, and reminds us that sometimes the most difficult journey is the one that takes us home.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.344 | 0.212 |
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".