Bibliographic record
Abstract
What life to lead and where to go After the War, after the War? We'd often talked this way before. But I still see the brazier glow That April night, still feel the smoke And stifling pungency of burning coke. I'd thought: 'A cottage in the hills, North Wales, a cottage full of books, Pictures and brass and cosy nooks And comfortable broad window-sills, Flowers in the garden, walls all white. I'd live there peacefully and dream and write.' But Willie said: 'No, Home's no good: Old England's quite a hopeless place, I've lost all feeling for my race: But France has given my heart and blood Enough to last me all my life, I'm off to Canada with my wee wife. 'Come with us, Mac, old thing,' but Mac Drawled: 'No, a Coral Isle for me, A warm green jewel in the South Sea. There's merit in a lumber shack, And labour is a grand thing...but--- Give me my hot beach and my cocoanut.' So then we built and stocked for Willie His log-hut, and for Mac a calm Rock-a-bye cradle on a palm--- Idyllic dwellings---but this silly Mad War has now wrecked both, and what Better hopes has my little cottage got?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.874 | 0.722 |
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".