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
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it