Savagery and the Heart of Darkness in William Golding’s Lord of the Flies
Bibliographic record
Abstract
William Golding’s first-hand experience of battle-line action during World War II “was to shock him into questioning the horror of war. These experiences inform his writing; he was appalled at what human beings can do to one another, in terms of the wartime atrocities…and in their being innately evil” (Foster,7) Two important elements of Golding’s life and experience are powerfully reflected in Lord of the Flies – his pessimism after the Second World War and his insight- as a schoolmaster into the way children behave and function; these two elements from the focus of examination in this paper. What happens when boys are left to their own devices? Golding implies a radical less optimistic view of human nature and civilization. More explicitly, he uses a Pacific island to symbolize the condition of humanity. “Having clinically insulated life on the island from the world and thus contrived a microcosm, he magnifies and inspect it. By this method he examines the problems of how to maintain moderate liberal values and to pursue dis tant ends against pressure from extremis ts and against the lower instincts.” (spark notes.com) Keywords : Tropical island; Children; Beast; Heart of darkness; Savagery
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".