The USV Annals of Economics and Public Administration Volume 13,
Bibliographic record
Abstract
In this article an author conducted the analysis of labour in the public sector of Canada after such nine sub-groups of establishments: 1) federal general government; 2) provincial and territorial general government; 3) health and social service institutions (provincial and territorial); 4) universities, colleges, vocational and trade institutes (provincial and territorial); 5) local general government; 6) local school boards; 7) federal government business enterprises; 8) provincial and territorial government business enterprises; 9) local government business enterprises. On the basis of statistical information about these sub-groups for 2007-2011 from a web-site «Statistics Canada » the maximal and minimum values of such three indexes are found: amount of employees, general annual sums of wages and annual sums of wages per employee. Rating for nine sub-groups of establishments of public sector of Canada on these indexes is certain. The got results testify, that during an analysable period most of the employees of public sector was concentrated in health and social service institutions, the least – in local government business enterprises. In 2007– 2011 a most general sum was earned also by the employees of health and social service institutions, the least – by the employees of local government business enterprises. At the same time in an analysable period among the state employees of Canada a most wage in a calculation on one person was got by the employees of federal general government, the least – by the employees of local general government.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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".