Bibliographic record
Abstract
In a theoretical exploration of the notion that societies often construct marginalized groups and/or persons as criminal Other, not only through hegemonic discourse but through various legal forms, this thesis engages the question of how laws help to construct a particularized notion of specific racialized groups as Criminal Other. The study draws on previously published research that focuses on the issue of deportation from Canada, and examines the ways in which deportation policies, designed to serve the domestic interests of powerful states, impact on nations with relatively little power to escape the global reverberations of such practices. Located within an emergent field of transnational criminology, this thesis extends beyond the confines of the nation-state that has traditionally constituted the realm of criminological enquiry and engages a cross-national perspective that questions the possible implications of "domestic" policy on regional, and increasingly global, dimensions of security. The data presented extends the field of knowledge in this area of research with an analysis of the effects of Canada's 1995 deportation provisions, as seen from the perspective of those deported, and from a vantage point that considers the impact of such policies on receiving countries. The study's original contribution to the subject area is two-fold. Firstly, it examines the previously unexplored international implications of Canadian deportation policy through a documentation of its potential impact on another sovereign nation. Informed by post-colonial and critical race theories, this study is perched on the margins of an exploratory transnational theoretical framework that focuses on the use of deportation as a method of crime control, and examines the effect of the relocation of large numbers of criminal offenders on Jamaican society. Secondly, the study fills a gap in the current literature by providing primary research data on a vulnerable population, and aiding in a process of "coming to voice" for those whose stories often remain untold. Through an in-depth examination of the lives of the deported foreign-born criminal offender, the study pursues a path that seeks to encourage further criminological enquiry on the under-explored yet vexed issue of deportation and its discontents.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".