Bibliographic record
Abstract
Covid-19 has exposed too many weaknesses in the neoliberal capitalist system to count, especially when it comes to the most vulnerable. For ten years our international, interdisciplinary research team has been documenting the profound weaknesses in nursing home care within Canada, Germany, Norway, Sweden, the UK, and the US. Many of the current deficits in resident care can be attributed to various forms of privatization at the centre of neoliberalism. Especially in Canada, the UK, and the US, nursing homes that are heavily funded by the public purse have been handed over to corporations, providing them with guaranteed pay and, in Canada at least, guaranteed full houses. The lines between for-profit and not have become increasingly blurred by various neoliberal strategies. One of these involves non-profit and state-owned homes contracting out services to for-profit firms as – in denial of the literature on the determinants of health – services such as food, housekeeping, and laundry have been defined out of care and dismissed as ancillary. This contracting out has not only undermined teamwork, but has also resulted in poor food, inadequate cleaning, and limited laundry – all of which threaten health. At the same time, fewer and fewer spaces are available in these homes with government funding. The result is twofold. All those who manage to get into these homes have high care needs, and those who cannot are either forced into the for-profit sector or rely more on unpaid care, most of which is provided by women. For too many, neither of these is an option. Another strategy blurring the lines is the promotion of for-profit managerial strategies within the non-profit and public nursing homes that remain. This means the lowest possible staffing levels, the shifting of as much work as possible to those with the least formal training, limiting workers’ autonomy, pay, hours, and benefits, and relying on a labour force already made vulnerable by gender, racialization, and immigration status. Barely enough pre-pandemic proved simply not enough during the pandemic – which exposed the disastrous consequences of all these developments.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".