Deconstructing Decriminalization: A Genealogy of the Provincial Offences Administration, the Modern Fine, and Penalization in Ontario
Bibliographic record
Abstract
WDW -Who Does What Panel WPR -What's the Problem Represented to Be v and ability to reason through the various complex issues we face, while you always seem to hold me up to some higher standard, nothing could be father from the truth.You are always by my side, pushing me on just as much as I push you.Mom, thank you for your strength, courage, wit, humour, and love, as always it is unwavering and something I will always cherish.Finally, to the most important person in my life (sorry brother), my partner, my fellow gadfly, my Buddha: Shanisse Kleuskens.Alas, I do not know what the world would be like without you.While my brain at times like Abed considers the potential of multiple timelines and the existence of parallel universes, I am fully aware that the one in which we are currently existing, your love and support -much like Samwise Gamgee to Frodo Baggins -was vital to me realizing this goal and dream, and thus, does not go unnoticed.I never imagined finding a partner who would share my love for criminology, belief in people, justice, and openness for adventure while simultaneously helping me become a better man and person, introduce me more intimately to the works of David Garland and Georg Rusche, all while being a younger yet wiser individual.I know your genealogy extends just like mine (as Foucault reminds us!), from your Kleuskens clan (i.e., sisters, parents, Nana) and beyond, but I am so proud of everything you do.I love you and cannot wait to see where our life journey takes us next.With that said, I would lastly like to point out that I take full responsibility for any errors or inadequacies that may remain in this thesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".