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. The big painful jolt of demonetisation creates the right psychological milieu for the war against black money to start. Only Time will tell whether steps such as the Income Declaration Scheme (IDS) in the Budget for 2016-17, the August 2016 amendment of the Benami Transactions (Prohibition) Act of 1988, and the Taxation Laws (Second Amendment) in November 2016, are parts of a concerted plan for tackling black money, and this time is different from 1946 and 1978. With the strides made in digitisation of tax returns and bank records together with PAN, Aadhar and KYC regulations, compared to 6 per cent in 1946 and 11 per cent in 1978, at least 15 per cent or INR 2.2 trillion of the demonetised notes not exchanged into deposits or cash will provide a preliminary positive feedback on the success of the current demonetisation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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 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".