Projection of population and industrial employment in the Vancouver C.M.A.
Bibliographic record
Abstract
The goal of this thesis is to draw together the principal factors affecting regional growth and to apply this knowledge to the Vancouver C.M.A. This involves three objectives. The first objective is to review the literature pertaining to the theory of regional growth. This review appears in Chapters 1, 2, and 4. These chapters constitute an attempt to draw together important factors from an extremely wide field. This work is designed to provide a framework for evaluation of the state of the art in regional projection models. The second objective is to review the literature to find examples of practical applications of the above theory. Specific examples of population and economic models for projection are examined and their major features are presented in Chapters 3 and 5. A model based on the state of the art is created and reported in Chapter 6. This work involves the use of available regional sectoral employment data and a shift-share model to produce employment projections. These employment projections are then combined with an estimate of the historical relationship between migration and employment in the region to produce an estimate of projected migration. The projected migration is an input to the cohort survival model which projects future population. A check on the consistency of the estimates of population and employment is provided by the calculation of the expected employment rate. In conclusion, the literature review reveals the complexity of the phenomenon of regional growth. It also reveals that large scale computer models have not been highly successful in dealing with the problem. This leads to the conclusion that simple models are more desirable for projection purposes. However, in the present case, the simplicity of the shift-share model is perhaps too great. The model, in general, explains less of past growth than does a simple time trend extrapolation model. The population model, while also simple in structure, performs much better. In that case the major factors affecting population growth (fertility, mortality and migration) are treated separately in the model. It would be desirable to explain the separate components of regional economic growth (labour, capital, technical knowledge). The major limiting factor to this task is the quantity and quality of regional data available. Until this is improved, regional analysts will be confined to unsatisfactory projection models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".