Bibliographic record
Abstract
With a population of just over 1.4 billion, India has just become the most populous country in the world, and the seventh largest by land area (3,287,263 sq. km). India has enjoyed a rapid recent rise to prominence on the world stage, both politically and economically. Yet very little is known by “Western” scholars about naming in India, whether naming of people or of places. India is a very diverse land, with many cultures, religions, languages, climates, and geographies. Added to this are India’s colonial past (British, French, Portuguese), various other rulers and influencers over the years (e.g., Mughals), social factors such as the caste system, all leading to very complicated systems of naming, with much regional and ethnic variation. This paper will give an overview of relevant history and colonial influences, before moving on to several phases of post-colonial renaming/respelling of toponyms (e.g., Bombay/Mumbai, Madras/Chennai). I will then turn to personal naming systems, looking at different systems as determined by social class and caste, religion, gender discrimination, and other features such as northern (Indo-European) vs. southern (Dravidian). Throughout, there will be attention to the sociological and sociopolitical contexts of contemporary India, as well as the influence of English and “Western” culture.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".