Madott, Darlene. Winners and Losers: Tales of Life, Law, Love and Loss.
Bibliographic record
Abstract
Darlene Madott's Winners and Losers fashions a world where characters inhabit spaces of ambiguity, where right and wrong, good and bad, winner and loser are not opposing but rather simultaneous and overlapping states of being.In this book, Madott's ninth, interlinked short stories centre on Francesca Malotti as she navigates corresponding and competing commitments to the legal profession and to family.As readers move through twenty stories, including an epilogue that powerfully blurs the boundary between author and protagonist, readers encounter non-chronologically various moments of Francesca's life, from glimpses of her childhood through to her experiences of law school, early articling, marriage to and divorce from a man with persistent financial problems, balancing her cases with single-parenting, and the early days of retirement.The variety of this collection's stories coupled with the unifying presence of a singular protagonist propel readers forward through compelling contemplations of what it is to be just and serve justice in both our professional and personal lives.The characters that Madott creates are a key strength of this collection.Madott's exploration of the fallibility and fragility of the human spirit hinges on the "and" in the collection's title: each individual is both winner and loser, their depth emerging in that in-between space of "and." The collection's first and eponymous story, "Winners and Losers, " sets the stage with Francesca simultaneously celebrating a win for her client in a custody matter and acknowledging her and her young son's own loss of time with each other because of her work commitments."Betrayal" centres on a young woman, the wonderfully named Margaret Meanie, who mentors the older Francesca while they article at a large law firm.The story's title may imply judgement, but as this story and the rest of the collection showcase, verdicts are rarely static; doubts and uncertainty remain reasonable.In the end, Ms. Meanie may be the winner, outsmarting her fellow articlers, but she too is the loser, shedding bits of integrity along the way; Francesca, on the other hand, may be the loser, manoeuvred out of a job, but she too is the winner, forfeiting the game, not herself.In this collection's focus on the moral complexity of human existence, Francesca must often confront her attraction to flawed individuals.These are characters who are interesting, even alluring, because of their weaknesses, even though these weaknesses could also reasonably be met with
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".