A sociology of hope: why we need a radical action agenda for social justice
Bibliographic record
Abstract
In April 2013, Canadian Prime Minister Stephen Harper responded to questioning about a thwarted terrorist attack by claiming: “It’s time to treat these things as serious threats…. this is not a time to commit sociology” (National Post, 2013). At the time, he and then-candidate Justin Trudeau had debated the merits of looking for root causes to social problems. Instead, Harper held fast to his administration’s focus on punishing more criminals with harsher sentences to stop crime. His colleague, Conservative MP Pierre Poilievre, doubled down on Harper’s anti-intellectualism, suggesting that while there is nothing necessarily wrong with trying to understand why terrorism happens, he deduced, “The root cause of terrorism is terrorists” (Fitzpatrick, 2013). Just over a year later, Harper would reiterate his “penal populism” (Pratt, 2007) in the case of a murdered Native Canadian teen, Tina Fontaine. Despite the demand of Canadian First Nations for a federal inquiry into the disappearance of over 1,100 aboriginal women, Harper insisted that these were each individual criminal cases, not a “sociological phenomenon.” As social scientist and nongovernmental organization (NGO) activist Craig Jones (2015) explained, penal populism represents: [the right-wing] politicization of criminal justice and drug policy for short-term electoral advantage combined with a sympathetic— but largely content-free—discourse about “victims” amounting to a degradation of our justice system…. [It is] characterized by open hostility toward evidence, disdain for harm reduction, and contempt for science, and disinterest in what works to limit the damage from incarceration, drug prohibition and drug use.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.133 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.022 | 0.043 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".