Bibliographic record
Abstract
Telling Lives in India: Biography, Autobiography, and Life History is editedby David Arnold (professor of South Asian history) and Stuart Blackburn(research associate), both of the School of Oriental and African Studies(SOAS), London. The intellectual contributions of the editors and nine otherdistinguished scholars, all of whom belong to a range of academic disciplines,make this collection of eleven essays a remarkable and highly readablework on life histories – biographies, autobiographies, and oral accounts– from India. This volume grew out of the “Life Histories” project establishedat SOAS and out of various workshops held between 1998 and 2000at SOAS, the London School of Economics, Oxford University, CambridgeUniversity, and the British Library.In their well-thought-out and written “Introduction,” the editors explainwhy this volume was published. According to them, for a very long time thelife history approach has been gaining wide acceptance among scholarsbelonging to various disciplines, such as women’s studies and black studies,due to a “growing distrust of ‘meta-narratives’” and a firm desire to “movetowards a more nuanced, multi-stranded understanding of society and agreater recognition of the heterogeneity of human lives and lived ...
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".