“Westmount’s Sinai”: Projecting a Jewish Landscape onto Montreal through Fiction
Bibliographic record
Abstract
For Canadian Jewish authors, every peak and every valley, every lake and every island, every forest and every plain, is a potential locus for mythic energy. In this brief article, I wish to offer a glimpse into the implicit means by which Jewish authors project a specifically Jewish landscape onto their surroundings. Through a short study of Chava Rosenfarb’s Edgia’s Revenge and Leonard Cohen’s The Favourite Game, I will explore both authors’ uses of Mount Royal and the Laurentian Mountains as sacred spaces in the tradition of earlier Jewish stories involving mountains and wilderness. These similarities are especially poignant when we consider Cohen and Rosenfarb’s very different experiences of being Jewish in the world—one a wealthy uptown Jew from Montreal and the other a survivor of the Holocaust.Pour les auteurs juifs canadiens, chaque sommet et vallée, chaque lac et île, chaque forêt et plaine, est un lieu potentiel d’énergie mythique. Dans ce bref article, je souhaite offrir un aperçu des moyens implicites par lesquels les auteurs juifs projettent un paysage spécifiquement juif sur leur environnement. À travers une brève étude d’Edgia’s Revenge de Chava Rosenfarb et The Favourite Game de Leonard Cohen, j’explorerai les usages par les deux auteurs du Mont Royal et des Laurentides en tant qu’espaces sacrés dans la tradition d’histoires juives antérieures sur les montagnes et la nature. Ces similitudes sont particulièrement probantes lorsque nous considérons les expériences très différentes de Cohen et Rosenfarb de vivre leur judéité — l’un un juif nanti élevé à Westmount et l’autre une survivante de l’Holocauste.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".