Bibliographic record
Abstract
Chris Jones and Tony Novak speak eloquently about the expanding inequality and poverty now evident throughout the world and why all of us, especially social workers, should be moved to ‘clear thinking, strong words and action’. I begin this comment by identifying some shared points of analysis and also some differences when comparing neoliberalism in Canada to Britain. In response to Jones and Novak's call to action, I also address the issue of alternatives and the necessity of taking action. The Canadian context The story in Canada is remarkably similar with regard to the development and impacts of neoliberal discourse and policy implementation when compared with Britain. The differences that exist are largely due to Canada being a middle power with a much smaller economy and population, and due to our proximity to the US. In the Canadian context, US hegemony, in terms of economic integration and political and cultural influence, cannot be underestimated. Following Beveridge and Keynes post-World War II, Canada's social safety-net evolved in patchwork fashion and more slowly than in Britain but by 1966, for example, universal health care and social assistance had been achieved, along with significant investments in education and an expanding public sector. These social programmes lasted 30 years before being eroded or dismantled entirely in the mid- 1990s by the same federal Liberal Party that had brought them into existence. What remains today are decentralised fragments of social services that vary widely from province to province according to the degree of economic austerity being exercised. The economic crisis of the early 1980s, subsequent recessions and rising debt and deficits in Canada, which neoliberals used to justify cutting social programmes, were actually manufactured by the Canadian political and business elite. In the Canadian version of Thatcherism and Reaganomics, the Bank of Canada deliberately slowed economic growth with higher interest rates and adopted a policy of high unemployment even though inflation was low (Stanford, 1998, p 42). While this policy of ‘permanent recession’ led to significant deficits, which favoured the financial sector that serviced government borrowing, allowing 8.5% unemployment as a way to discipline labour costs signalled the end of state investment in job creation and of support for the victims of capitalism (Stanford, 1998, p 42).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.057 | 0.019 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".