The measurement of economic diversification with reference to regional unemployment
Bibliographic record
Abstract
Over the past four years, considerable attention has been focused on the problems of high unemployment in the State of Oregon. The percentage of jobless continued to be higher than that of the nation. The depressed housing market, caused by high interest rates, coupled with an increase in the import of Canadian timber managed to reduce the demand for Oregon lumber and wood products drastically. This has resulted in an abnormally high unemployment rate in many of Oregon's counties which are dependent on the wood industry; for example, the 1980 jobless rate in Harney County reached a record high of 29 percent. On the other side of the spectrum, less dependent counties such as Gilliam and Morrow continued to grow during the same period, with unemployment rates of merely 4.9 and 5.8 percent respectively. These rates are approximately half the state average. Community leaders, including the Governor, seem convinced that the only solution is economic diversification. It is an argument that makes a great deal of sense at first glance. The notion here is that if you diversify you will become less vulnerable to outside forces and hence will have a more stable economy. But what is diversification? How can we tell that one region is more diversified than another? Furthermore, having a diversified economy, does this ensure a lower rate of unemployment? The thrust of this dissertation deals with providing answers to these questions. Three schools of thought--ogive-norm, portfolio variance and entropy--were examined in an effort to determine a more proper measure of economic diversification. Various statistical procedures of hypothesis testing were employed together with stepwise regression and analysis of variance. The research findings indicate that there is a definite relationship between economic diversification and regional unemployment. However, only 28 percent of the change in the rate of unemployment is explainable by changes in the levels of diversification. (The necessary data were provided by the State of Oregon - Employment Division).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".