Bibliographic record
Abstract
Indian card industry has gone through several interesting changes in the recent past. We wanted to explore this fascinating world both from card issuing and merchant acquiring perspective within our chosen study period of around 6 years - from Q2, 2011 to Q4, 2016. However rather than hunting the data to prove any pre-defined notion we wanted to listen to the data to capture the story it wants to tell us. We took a canvas of raw primary data from a no. of sources ranging from Reserve Bank of India to World Bank, from Ministry of Finance to Ministry of Statistics & Implementation (of Govt. of India) and a no. of other sources like Yahoo Finance, Index Mundi data portal. As a choice of tool we have used R (an open source software) and Excel for our study. In order to uncover the underlying story behind the data we have used an array of techniques ranging from descriptive trend chart, heat map, multidimensional bubble plot, outlier chart to advanced data analytics techniques like clustering using machine learning algorithm. We also uncovered the correlations of card industry parameters with other economic and social indicators and went ahead in building optimum casual predictive model as well as time series forecasting model.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.056 |
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".