Bibliographic record
Abstract
A scathing indictment of austerity policy In 2016, Brian Pallister’s Progressive Conservative Party of Manitoba successfully campaigned on a platform to reduce taxes and restore the balance between revenue and spending. The years that followed their victory saw wages frozen, emergency rooms closed, intensive care unit beds reduced, healthcare jobs eliminated, Manitoba Housing funding slashed, and payments to foster parents decreased, as the civil service was diminished by 27 percent. Public Service in Tough Times gives voice to the people behind the balance sheets, shedding light on the vicious cycle of understaffing, burnout, attrition, and despair created by austerity policy. Using survey data from thousands of public sector workers and carefully compiled statistics on spending and staffing, editors Jesse Hajer, Ian Hudson, and Jennifer Keith, demonstrate how cuts to government expenditures disproportionately benefit the wealthy and exacerbate poverty and inequality. As the virtues of small government, tax cuts, and private sector investment continue to be the rallying cry of right-leaning politicians worldwide, this impeccably researched case study delivers a crushing critique of austerity and its consequences.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".