Bibliographic record
Abstract
[Please note this report is only available in Spanish] The IMF’s Statistics Department assisted Bolivia’s National Institute of Statistics (INE) in updating the quarterly national accounts, introducing a new base year of 2017 to improve the accuracy and timeliness of key economic indicators such as quarterly GDP. This partnership involved both virtual and in-person collaboration from late 2024 and throughout 2025, covering all steps from data selection to the analysis of preliminary results. The mission assisted INE in developing a new compilation framework, aligned with international best practices and the 2017 Quarterly National Accounts Manual guidelines, leveraging the annual accounts infrastructure to implement methodological improvements like chain linking. INE will continue to release updated statistics during the third quarter of 2025 to keep the public and policymakers informed about Bolivia’s economic trends. To ensure transparency, INE will publish methodological notes, an advance release calendar, and a formal revision policy, adhering to international standards for statistical dissemination. Regular revisions of the latest figures are recognized as a key part of the statistical process. The mission was funded by the Data for Decision (D4D) Fund.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".