The Sources of Taiwan's Regional Unemployment : A Cross-Region Panel Analysis
Bibliographic record
Abstract
In recent years under the trendof a global recession andd omestic structural change, the unemployment rate in Taiwan has reacheda recordhigh of above 5% which in turn has generateda series of social problems. Unemployment has now become the core issue in the government's agenda. From historical regional data, we findthat there are distinct variations of the unemployment rate among 23 cities andprefectures; moreover, this differentiation seems to persist over time. We analyze this regional unemployment trendby equilibrium and disequilibrium factors controlling for the macro environment using 23 cities' andprefectures' cross section and time series data pertaining to the 1995 to 2004 period. A cross-region panel study shows that the major factors that explain the persistent but divergent regional unemployment rates (aside from the aggregate macro environment which explains about one quarter) are demographic composition, family characteristics, industrial structure, population density, migration costs, and labor mobility. Understanding the sources of regional unemployment will help us to determine the appropriate policies to mitigate the unemployment rate across regions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".