Bibliographic record
Abstract
The U.S. economy’s high-tech sector (internet, computers, telecommunications, etc.) burst its classic speculative bubble in 2000. The Nasdaq stock market lost 40 per cent of its value during the year and lost another 20 per cent in the first quarter of 2001. The Nasdaq dragged down most other stock market indicators in the U.S. Trillions of dollars in U.S. wealth vanished. The wealthiest citizens turned away from the stock market as rapid losses replaced the absurdly high gains of 1999. Other U.S. citizens watched in horror as their recent expansions of securities holdings rapidly shrank in value (also confronting many with vanished savings and reduced retirement benefits since their pensions were invested in ‘history’s greatest boom’). See Appendix 5 for the details on U.S. stock ownership patterns. Industries began to scale back their investment programs as rapid growth shifted to slow growth and recession loomed. The majority of workers slowed their spending and their accumulation of debt because of falling stock prices and because they fear a recession’s impact on wages, benefits, and job security. All these negative developments are continuing into 2001.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".