Bibliographic record
Abstract
The purpose of this thesis is to develop a theoretical framework which could then be used to measure structural unemployment. This is done by first surveying the relevant literature, then developing a theoretical model for the measurement of structural unemployment, and finally applying this model to Canadian data. In the survey of the relevant literature the various approaches are categorized into the causal, the structural maladjustment and the policy approaches. The causal approach involves explaining structural unemployment in terms of the causes of labour displacement. This is considered inadequate because it ignores problems in the labour market adjustment process, whose function is to wipe out the imbalances created by structural dislocations. These problems are considered by the structural maladjustment approach, together with the symptoms of structural maladjustment. It analyzes the forces promoting and impeding the labour market adjustment process. However, these forces are at present not quantifiable. The symptoms of structural maladjustment, on the other hand, are. A favourite method of determining changes in structural maladjustment has been the analysis of the structure of unemployment. This thesis, however, supports the contention that this method is generally misleading. Analyses of long-duration unemployment are also considered not to be useful, but a framework involving the relationship of unemployment to vacancies is seen as fruitful. The policy approach is concerned with the relationship of unemployment and inflation. According to this approach, the degree of structural maladjustment is indicated by the distance of the inflation-unemployment function from the origin. However, there are problems involved in using it to measure structural unemployment, primarily because of the impurities involved in the relationship. The next step is to develop a model which does not depend on proxies for labour demand, but uses variables directly related to the labour market, and has a theoretical rationale. This model must separate the effects of aggregate demand and of structural imbalances on unemployment. It does this by determining the cyclical relationship between the unemployment and vacancy rates and attributing changes which cannot be explained by this relationship to changes in the level of structural imbalances. . This model is then applied to Canadian data. Before that can be done, however, the vacancy rate has to be derived from N.E.S. vacancy data. The ratio of actual vacancies to N.E.S. vacancies is estimated on the basis of the ratio of total hirings to N.E.S. placements. Using the vacancy rate thus estimated, several forms of the relationship between the unemployment and vacancy, rates are empirically tested. The results indicate that very little of the changes in the total unemployment rate are attributable to changes in structural imbalances. Variability in unemployment is largely caused by variability in aggregate demand. However, there appears to have been some upward trend in the structural unemployment rate (defined as the unemployment rate that would prevail if aggregate labour demand were equal to aggregate labour supply), from 3 per cent in the early 1950's to nearly 4 per cent in the 1960's. These results suffer from the uncertainty involved in the estimated vacancy rate, but an analysis of changes in the ratio of total hirings to N.E.S. placements, which was used in the estimation, supports the findings concerning the structural unemployment trend.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".