Bibliographic record
Abstract
There is a general sense that 1990s labour market was unique. It has been characterized by notions such as downsizing, revolution, the knowledge-based economy, job instability, and so on. This paper provides an extensive overview of performance of 1990s labour market, and asks just how different it was from 1980s. It goes on to ask if facts are consistent with many common beliefs and explanations. The paper focuses on (a) macro-level labour market outcomes, and (b) distributional outcomes. Macro-level topics include: has nature of work changed dramatically in 1990s? has there been a continued ratcheting up of unemployment? have we witnessed rising job instability and increased levels of layoffs? did company downsizing increase in 1990s? why did per capita income growth stall in 1990s? for a worker with a given level of human capital, has there been a deterioration in labour market outcomes? Much of focus in labour market over 1980s and 1990s was on distributional outcomes - who is winning and who is losing. Some of distributional outcomes of 1990s labour market addressed in paper include: outcomes for men and women; changes in relative wages of highly educated and earnings inequality; trends in rate of low-income; changing outcomes for recent labour market entrants, including young people and immigrants; and extent to which technological change plays a major role in these outcomes. The paper concludes with a discussion of overall performance of 1990s labour market as compared to 1980s.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".