Trade collapse, data gaps and the impact of the financial crisis on official statistics
Bibliographic record
Abstract
Global merchandise trade collapsed in the first quarter of 2009 at an unprecedented rate but not evenly across the globe. Demand for durable goods in developed countries declined with prices of oil and minerals falling drastically. Disruptions affecting trade finance and international supply chains were often quoted as a contributing factor to the steep fall of trade flows. While trade in transport and travel services also dropped, trade in other commercial services showed more resilience (apart from financial services). Many economists were taken short by these developments while some had warned as early as 2003 that global imbalances may lead to a meltdown of the financial system. 2 While it may be discussed why economists ' were short of forecasting this global recession, the question that needs to be raised for statisticians is whether relevant statistics have been provided, that is: 1. Do statistics describe economic reality adequately? Do statistics offer information that helps monitor the most recent economic developments?
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 | 0.030 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".