Bibliographic record
Abstract
Whether we are touched by the 2015 migrant crisis in the Mediterranean or the heated debates about the status of the (260+ million) displaced persons in our different societies, all of us have been affected by the «age of migration.» Marco Micone’s hybrid text, which through this translation will now be available to English readers, is made up of autobiographical snapshots, brief commentaries, and a short theatrical exchange. It includes the author’s own childhood experiences in Italy and his emigration as a teenager with his family to Québec. The author’s clear-sighted, often tongue-in-cheek descriptions continue to be relevant today, not least when he explores the challenges of the Canadian policy of multiculturalism and Québec’s decision to choose a different, «intercultural» model to defuse the springing up of ethnic village-like ghettos, particularly in urban centers like Montréal. His promise to the Francophone Québécois that «one hundred peoples coming from afar» would ensure that the French-speaking community could endure within the North American context, has been borne out by his own texts. The author writes with passion, with sincerity and, as literary critic Gilles Marcotte notes, with an intelligence that often helps to stretch the reader.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".