Bibliographic record
Abstract
The way I write — by which I mean both the practices I follow and (please God) the style of my writing — has changed over the years: though, as I tell all my students, that doesn’t mean it’s become any easier.I wrote my PhD thesis (on the woolen industry in Yorkshire between 1780 and 1840) in three weeks. Really. Starting at 7a.m., with thirty minutes off for lunch (including a walk to the corner shop for a newspaper, trailed by our deeply suspicious cat all the way there and all the way back), an hour off for dinner and the quick pleasure of a novel, knocking off at midnight. Every day for twenty-one days. When I finished I promised myself I’d never work like that again. Years later, while I was writing The Colonial Present, I became wholly absorbed in the attempt to keep up with a cascade of real-time events in multiple places. My training as an historical geographer hadn’t prepared me for that — I’d always envied the ability of colleagues writing about contempo-rary issues to make sense of a world that was changing around them as they wrote — and there were times when I yearned for the less frenetic pace of archival work. But I wasn’t writing to a deadline — though as the project swelled beyond an analysis of the US-led invasion of Afghanistan to include Israel’s renewed assault on occupied Palestine and then the US-led invasion of Iraq, I decided I must finish before Bush invaded France.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.159 | 0.108 |
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".