Bibliographic record
Abstract
This paper is not strictly academic.What I hope to foster is a discussion of the images created by a group of writers who have in common their identity as Italian Canadians, and who have written novels set within this cultural milieu.Like portraits in a gallery of diverse artists, the images of women we see in their writing vary in distinctiveness, detail, and purpose.What do we, the readers, expect when we enter the gallery, that is, when we pick up the book?Do we have an image in mind against which the text will be measured?Perhaps we are beginning with the Mona Lisa, the timeless, classic features of serene beauty concealing emotion, sensuality under control.Or we do imagine that the woman in black, the Nonna sitting beside us on the bus, is the figure we will encounter on the page?What about the gifted artist?The honored community leader?The housewife?Will we see ourselves?In short, do we pick up the book with a stereotype in mind before we open the cover?I ask that question to draw the audience into the tension that exists right now between Italian Canadian writing and its sociological context.The community is present and distinct; the writers are also distinct among their contemporaries.The images ring true, but they are not to be taken as limitations.Each variation described above is available in the pages of the novelists and poets currently identified as Italian Canadian writers.In this paper I will refer to Caterina Edwards, Frank Paci, Darlene Madott, Nino Ricci, Mary Melfi, Mary Di Michele, and Marco Micone.There are, of course, others to cite, but just as the Group of Seven forms the core of Canadian painters, these writers appear as the main constellation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".