Bibliographic record
Abstract
The predictions in the media and from think tanks sound altogether alarming: Saskatchewan, with its booming economy, could be facing a worker shortage so severe that it could drastically hobble the province’s ultimate economic potential. While the world craves only more of Saskatchewan’s abundant natural resources, the province won’t possibly be able to keep up, due to a scarcity of workers that could be as significant as one-fifth of the labour supply by 2020. The Saskatchewan government has rushed to analyze the predicament, issuing reports that urgently seek solutions. But it hasn’t really developed any solutions. In fact, it hasn’t done much about the supposedly looming crisis at all. And that, actually, might just be all it can — and should — do. In truth, Saskatchewan can’t be sure it will be facing a serious shortage, or any shortage, at all. And any attempt by the provincial government to substantially intervene in the labour market could cause more problems for employers and the economy, than it addresses. Saskatchewan’s labour market has already shown a remarkable ability to adjust, on its own, to the commodities boom, and what employers today call a shortage, could well just be everyone getting used to a much tighter, but still very functional, labour market. The province’s lack of action did mean it missed a once-in-a-lifetime opportunity to redirect a huge cohort of Gen-Y students into training for trades that are in high demand (that cohort is already in its mid-20s and finished, or finishing, its career training). That was a mistake. But one big thing the Saskatchewan government can still do to help employers — and workers — is to stop making the strains on labour worse by launching imminent public infrastructure projects that compete with the private sector for labour. Instead, the province should plan those for when the boom slows down and workers need the jobs. It should also abandon any ideas of ramping up the import of temporary foreign workers to fill short-term job vacancies: those workers have a way of dampening wage signals that would draw more permanent, and therefore desirable, workers to the province. What few things the province could be actively doing, it should do anyway. It should help retrain workers with skills in low-demand for jobs in higher demand. It should recruit migrants from other provinces and overseas to settle in Saskatchewan. It should carefully review its post-secondary education system to minimize drop-out rates from apprenticeship programs and to ensure it is training people to match the economy’s demands. And it should be finding ways to mobilize large portions of the population that could be working, yet aren’t, including underemployed males and Aboriginals, but also the elderly and disabled. If there is, indeed, a shortage somewhere in Saskatchewan’s future, having those people working can only help. But even if there is never a shortage, having large pools of potential labour sitting idle is something that will truly limit Saskatchewan’s economic potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".