Bibliographic record
Abstract
Demonetisation of INR 500 and INR 1, 000 notes in India on November 8, 2016 is different from many other countries’ scrapping of high value notes in two respects – the withdrawal of their legal tender status and continuation with INR 1, 000 and INR 2, 000 notes. It has resulted in a cash shortage. Non-cash medium of payments may be encouraged by this shortage, but, with supplies only from the domestic currency presses, the shortage is unlikely to disappear by the end of 2016. Import of currency printed abroad may provide a solution for ending it sooner. The impact of the shortage, if it continues, will be fully felt in the last quarter of 2016-17. Its growth impact in 2016-17 is 0.7-1.3 per cent depending on how much shortage continues and for how long. [NIPFP Working Paper No. 184].
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".