Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This 2023 Article IV Consultation highlights that against the background of a strong economic performance over the last quarter of a century, Peru has been hit by multiple shocks in the last several years. Adequate policies and very strong policy frameworks have made the economy resilient. Growth is expected to slow to 2.4 percent in 2023 and converge to its potential of 3 percent over the medium term. Inflation is expected to decline gradually into the target range by end-2023-early 2024. Risks to the outlook are tilted to the downside, with key risks including escalation of Russia’s war in Ukraine, an abrupt global slowdown and commodity price volatility, monetary policy miscalibration by major central banks with a possible de-anchoring of inflation expectations and systemic financial instability, an intensification of political uncertainties at home, social unrest over political developments, and natural disasters. Financial sector policies should continue to maintain a tightening bias to cement financial stability in a deteriorating financial environment. The Organization for Economic Cooperation and Development accession process should be used to define a well-articulated structural reform agenda to deal with the scarring effects of the coronavirus disease 2019 pandemic and support green and inclusive growth.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it