Down Inside: Thirty Years in Canada’s Prison Service by Robert Clark. Fredericton: Goose Lane Editions (2017) 277 pp.
Bibliographic record
Abstract
R obert Clark is known among the prisoner sub-class as a cultural whisperer who denounces the toxicity manifested by the government body charged with our custody and care while incarcerated.His compelling personal memoir is a scathing indictment of bureaucratic indiff erence and agenda-driven administrative policy in relation to federally incarcerated Canadian citizens.During his 30-year career, Robert Clark rose through the ranks of the Canadian federal penitentiary system from student volunteer to deputy warden as he dealt with escapes, lockdowns, murders, deaths by suicide, and a riot.Inside this indexed account he challenges the belief that a "tough-on-crime" approach makes prisons and communities safer, arguing instead for humane treatment and rehabilitation.Down Inside diagnoses many of the symptoms of inertia found in the penitentiary system that often defy logical arguments predicated on an understanding of both the Commissioner's Directives and Canadian Charter of Rights and Freedoms.According to Mr. Clark, not only did these basic explanations fail to suffi ce many of his fellow employees, but he also observed that many people at the top would not risk poor relations with their staff or unions to ensure that every prisoner's rights were respected.In his opinion, the corporate culture is largely responsible for most of the problems that occur within our prisons.From his fi rsthand experience, he believes that the organization itself almost unoffi cially sanctioned the mistreatment of captives.In addition, Clark helps to frame his rationale objectively by stating in the introduction that the correctional system does employ many fi ne people who do deserve great credit for doing their best everyday under often impossible circumstances.The Conservative government's "tough-on-crime" agenda prompted his retirement in 2009 after he saw several unconscionable changes impacted our prison environment that made it more closely resemble the US model.Apparently aghast at how readily the leadership embraced these draconian measures, seemingly without any hint of moral confl ict, he decided that he had a duty to alert the world to what was going on inside the federal
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.031 |
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".