Bibliographic record
Abstract
This essay does not ask whether or not we need immigration- that is a separate question-but instead asks two core questions about Manitoba’s Provincial Nominee Program. First, is the program serving its original intent? In other words, are actual labour market needs viably connected to the immigrants who are chosen by the program, ready to work within the skill set that brought them to Manitoba in the first place? If they are not, what, if any, economic penalties are imposed and upon whom? Some possible economic effects upon the local economy and upon the migrants themselves are discussed. The second question that is posed is the wisdom of relying on immigration to solve the issues attached to that of an aging population, such as who will care for our aging society, and how diminishing numbers of workers will be able to bear the financial burden of doing so, if they are the only solution to this problem. Current ideas around demographic trends and the long-term implications for Manitoba and Canada suggest that immigration alone will never work. Increased productivity, changing retirement age, allowing pension contributions past 65 are ideas that should also be considered. This essay does not seek to criticize the program per se, but rather to question the policy that appears to suggest that immigration is ‘the ’ answer to the problems looming, rather than one of a suite of tools that should be used together, and perhaps even equally. i
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.473 | 0.267 |
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".