Bibliographic record
Abstract
Writing Our Way Home is an important contribution to\nliterary studies. “Italian-Canadian writers are not just\nCanadian writers, but world writers,” states literary critic\nElena Lamberti in the introduction to Writing Our Way\nHome. “They write from Canada with an original point of\nview on multiple (hybrid) identities and have something to\ntell the whole world ...” A unique volume of creative and\ncritical texts, Writing Our Way Home features contributors\nfrom Canada, Italy and the United States: Annalisa\nBonomo, John Calabro, Michele Campanini, Licia\nCanton, Maria Giuseppina Cesari, Pietro Corsi, Domenic\nCusmano, Marisa De Franceschi, Mike Dell’Aquila,\nAlberto Mario DeLogu, Delia De Santis, Gil Fagiani, Nino\nFamà, Venera Fazio, Frank Giorno, Gabriella Iacobucci,\nElena Lamberti, Maria Lisella, Ernesto Livorni, Darlene\nMadott, Michael Mirolla, Caroline Morgan Di Giovanni,\nLinda Morra, Oriana Palusci, Gianna Patriarca, Maria\nCristina Seccia, Maria Tognan, Osvaldo Zappa, Jim\nZucchero.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".