Anu Kapur, Mapping place names in India, Abingdon & New York: Routledge, 2019, xi + 234 pp., ISBN 978-0-367-14918-5
Bibliographic record
Abstract
The study of the place names of India seldom reaches the attention of "Western" scholars 1 .This was part of my motivation for choosing naming in India as the topic of my keynote at ICOS 2021, held virtually by the University of Krakow, and now published as Embleton (2023).What work there is is mostly produced by linguists (such as myself) or consists of articles on particular newsworthy topics in the popular press.Thus this book is a very welcome addition to the literature, especially as it is readily accessible outside of India.Its author, Anu Kapur, is a Professor of Geography at the Delhi School of Economics, University of Delhi, and thus she approaches the topic from a different angle from many onomasticians.The book is well written, easy to understand, and does not require any prior knowledge of India to fully appreciate most of it.Yet, even for those who know India well, there will be much to learn.The book consists of a foreword by Gopal Krishnan from Panjab University in Chandigarh, nine chapters, eleven figures, and 15 tables.Krishnan's foreword (viii-xi) situates this book relative to Kapur's previous work on goods carrying an official "geographical indication".He points out that in geography, "every description […] begins with a place name", but "a search into the origin, meaning and essence of place names is often bypassed" (ix).He foreshadows the interplay of historical, political, social, religious, ethnic, and linguistic factors in naming and renaming.Chapter 1 ("Place Names", 1-43) is unusually detailed and expansive for an introductory chapter with respect to the topics it covers.It begins with various definitions of the term "place name", with more attention to geographical aspects than usual, and builds into broader topics, such as association with products, connection to hobbies and fiction-writing, how names bond people with place, how place names trace history, how multiple names for the same place can create inclusivity or tension, estimates of the number of place names in India, alternates to place names (latitude and longitude specifications, various types of code and numbers), Pāṇini's study of place names, the importance of place names for mapmaking, and wondering why geographers in India are not more involved in place name study, a chapterby-chapter summary of the book, and thanks and acknowledgements.It ends 1 Perhaps this is why I have only found one review (Basik 2020) and one newspaper column (Sharma 2020).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".