Bibliographic record
Abstract
Since the late 1960s, Michael Pearson’s work has been at the forefront of thestudy of the Indian Ocean World. Pearson’s unparalleled contribution to thefield has long been recognized by his pears. In 1981, the famed historian ofGoa, Teotonio R. de Souza, wrote in an introduction to one of Pearson’s booksthat it ‘will stand out as the best effort on the part of a non-Indian historianto do justice to the Indian component of Indo-Portuguese history.’ In 2004,Pearson spoke to this acclaim in an interview with Frederick Noronha, a journalist-publisher based in Goa. He said: ‘Certainly this is what I have wantedto achieve when I write about the Portuguese in India: to locate them in theIndian context in which they operated and by which they were constrained.This is a deliberate attempt to counter the triumphalism, and even racism, ofmuch Portuguese writing on their empire.’ But Pearson’s influence was notlimited to Goa and the coastal western India. Across nearly four decades ofwork, Pearson was always a leader in developing the longue durée approach tostudying the Indian Ocean World.To honor this influence, the editors of the Journal of Indian Ocean WorldStudies have compiled an exhaustive bibliography of Michael Pearson’s work.They have also appended short descriptions to some of his most importanttexts. Limited space meant that abstracts could not be attached to each reference. The editors decided that where they existed, abstracts written by Pearson or his co-editors would be prioritized. They then selected some of his works without abstracts to write their own abstracts or mini reviews (indicated with **). Particular prominence has been given to some of his earlier, lesser-known works. The intention was to use the space to reflect the diversity of Pearson’s research, while highlighting some of its core themes.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.143 | 0.121 |
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".