‘Even a Successful Lawsuit Will Be a Worry’: Law and Community Relations in L. M. Montgomery’s Life and Work
Bibliographic record
Abstract
Law plays only a peripheral role in L.M. Montgomery’s fiction. Lawyers occasionally appear as supporting characters and the odd plot point turns on a legal document such as a will. But when conflict surfaces in Montgomery’s fictional communities, her characters rarely turn to law either as a means of dispute resolution or as a force to punish transgressions. Initiating legal action, even to assert admitted rights, is generally frowned upon within these communities. Disputes between individual characters and between and among families are ideally resolved within the community without resort to lawyers or courts. Montgomery was not able to sidestep legal conflict in her life in similar fashion. In her professional life, she endured a series of lawsuits against her first U.S. publisher L.C. Page that began in 1917 and extended for over ten years. A lawsuit also dominated her personal life between 1921 and 1923, and continued to preoccupy her for several years thereafter. This was a suit brought by a neighbour, Marshall Pickering, against Montgomery’s husband Ewan Macdonald following a 1921 car accident. Montgomery experienced the accident, served as a witness in the trial that followed, and wrote about it at length in her journal. In this paper, I explore the contrast between the role that legal conflict played in Montgomery’s life and in her fiction.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.064 | 0.057 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".