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
Abstract In this paper, we are motivated by the fast growing literature that investigates the performance of Divisia monetary aggregates. We construct Divisia monetary aggregates for India using monthly data form January 2001 to March 2020 and present a comprehensive comparison across the Indian Divisia monetary aggregates at four levels of monetary aggregation, M1, M2, M3, and M4. We do so in the context of three classes of empirical models. In particular, we compute correlations between the cyclical components of the Divisia monetary aggregates and the cyclical component of the industrial production index. We test for Granger causality running from the Divisia monetary aggregates to industrial production. We also test for time-varying Granger causality. We find that the levels of the Divisia monetary aggregates Granger cause economic activity in India during normal times, but the causal link broke during and in the aftermath of the extremely unusual circumstances of the Covid-19 crisis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it