Structural Adaptation of Atlantic Provinces: Growth of Employment and Output 1987-1997
Bibliographic record
Abstract
As the Canadian economy, like most industrialized economies, is becoming increasingly “knowledge-based” and “technology-driven,” the economic challenge facing Canada in general and Atlantic Region in particular is essentially that of improving and strengthening the relative position of employment and output. Each region has its own key sectors that perform well above the national average and sectors that lag behind the national average. Aggregate data mask profound trends in the employment pattern and sectoral output both at the national and the sub-national level. Canada’s productivity performance is the result of overall economic performance of the sub-national economies. A thorough understanding of the changing pattern of industrial activity at the provincial level, and hence employment shifts and output growth across the provinces is vitally important. Consequently in this study we endeavor to analyze and delineate the changes in the growth of employment and output in Atlantic Canada in the past decade. A modified version of the Shift-Share model, as developed by Stilwell [1969], is used for an in-depth analysis of recently available 16 sectoral classifications of employment and 15 sectoral classifications of output. 1 These classifications include the recently emerging sectors of the new economy along with traditional sectors. The first section describes the methodology, aims and shortcomings of the Shift-Share technique. The second section specifies the modified version of the model. The results are presented in the third section and finally the conclusions of the study are presented. Methodology 2 Employment and output are not distributed evenly across the country. The patterns of their distribution are often ambiguous and require strenuous interpretation. To understand the changes in employment and GDP dimensions at the provincial level requires some descriptive and analytical approaches. The measurement of changes in these patterns and evaluation of such changes requires that some standards be defined. For example, what is a favourable or unfavourable industrial mix
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".