Bibliographic record
Abstract
In this interview, Fulvio Caccia, an Italian-Canadian Governor General’s Award–winning francophone poet, novelist, and essayist, reflects on his literary career spanning over four decades. A prolific writer who has published over two dozen books, Caccia discusses the underlying themes in his works and the unique challenges posed by different literary forms. Caccia acknowledges that he often explores themes of identity and belonging in his creative endeavours, readily unravelling the layers of the immigrant experience. In his search for self-discovery, he challenges conventional notions of nationality and cultural belonging. A Montrealer at heart, but now living in the Paris region, Caccia delves into the genesis of his novel, La coïncidence (Triptyque, 2005), which focuses in part on the École Polytechnique massacre, a mass shooting that occurred on the grounds of the University of Montreal on 6 December 1989, killing fourteen female engineering students and injuring many others. In the narrative, the author looks into the enduring impact of past traumas on present lives, the complexities of human relationships, and the interplay of fate, love, and tragedy. Beyond a mere discussion of this novel, which was translated from French into English by Robert Richard and published by Guernica Editions in 2015, Caccia offers in this interview profound insights into his poetry, which draws extensively from his experiences as the son of Italian immigrants.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".