Securing Borders: Detention and Deportation in Canada. By Anna Pratt (Vancouver: UBC Press, 2005, 220pp, £62.50 hb)
Bibliographic record
Abstract
Immigration has been connected to crime, and (in)security for many decades now in the media and other popular discourses. The arrival of migrants is often explicitly and implicitly framed as an unstoppable natural phenomena or invading hordes and terrorists, and their presence is discussed in terms of socio-cultural and economic burdens, problems of integration, unemployment and crime. Canada is, of course, no exception. This book, on migration, detention and deportation in Canada starts with an overview in which Pratt introduces the reader to the basic thrust of her thesis followed by a presentation of the draconian framework of migration (and related initiatives) in a number of industrially advanced countries as well in supranational entities like the EU. This part of the chapter is by no means exhaustive; however, it clearly shows the migrant-phobic mentality across the western world and the schizophrenic practice of the western world that on one hand contributes to the opening of the borders for goods, capital, and (specific) labour, and on the other ‘closes’ the border to people searching for a better life. In addition, the overview provides an account of the ideas on which this text is based.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".