Bibliographic record
Abstract
Inflation targeting as a framework for monetary policy has grown in popularity since it was pioneered by New Zealand, Canada and the UK in the early 1990s. At last count, as best I could tell some 20 to 30 central banks around the world employed some variant of inflation targeting as the guiding framework for their conduct of monetary policy. At the ASSA meetings last January, Andrew Rose presented a paper looking at the durability of monetary regimes and showed that, despite its relatively recent arrival on the scene, inflation targeting was by far the most durable of the regimes that countries have tried in the post WWII period (see Mihov and Rose (2007)). And indeed the performance of central banks that have adopted inflation targeting has been impressive, prompting some to ask whether this is indeed the holy grail of central banking.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.060 | 0.046 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".