Misinformation as a Barrier to Sound Policy Decisions
Bibliographic record
Abstract
There is a great deal of misinformation that gets widely circulated. This poses many problems for the management of economies in two ways. One is that much of the misleading information is widely publicised by those who benefit from it. Another is that policymakers naturally tend to assume that they can rely on published data. According to the national accounts (NIPA) data, in 1932 and 1933 US companies in aggregate made losses, but no loss was recorded in any quarter in the aggregate published results of companies included in the S&P 500 index. The chapter describes how the change in relative volatility is the natural result of the change in the way managements are remunerated. Through charts, the chapter illustrates GDP as published and adjusted for changes in house and share prices in the US, real equity returns 1801 to 2011 in the US and various other economics data.
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 | 0.096 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.038 | 0.031 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".