Bibliographic record
Abstract
‘Who ever thought they would one day be able to read Malcolm Lowry’s fabled novel of the 1930s and 40s, In Ballast to the White Sea? Lord knows, I didn’t’ – Michael Hofmann, TLS This book breaks new ground in studies of the British novelist Malcolm Lowry (1909–57), as the first collection of new essays produced in response to the publication in 2014 of a scholarly edition of Lowry’s ‘lost’ novel, In Ballast to the White Sea. In their introduction, editors Helen Tookey and Bryan Biggs show how the publication of In Ballast sheds new light on Lowry as both a highly political writer and one deeply influenced by his native Merseyside, as his protagonist Sigbjørn Hansen-Tarnmoor walks the streets of Liverpool, wrestling with his own conscience and with pressing questions of class, identity and social reform. In the chapters that follow, renowned Lowry scholars and newer voices explore key aspects of the novel and its relation to the wider contexts of Lowry’s work. These include his complex relation to socialism and communism, the symbolic value of Norway, and the significance of tropes of loss, hauntings and doublings. The book draws on the unexpected opportunity offered by the rediscovery of In Ballast to look afresh at Lowry’s oeuvre, to ‘remake the voyage’.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".