Modern Mass Media and the Artist's Self-Disintegration in Fergus by Brian Moore
Bibliographic record
Abstract
Brian Moore (1921 –1999) was born in Northern Ireland. He immigrated to Canada in 1948, where he was a reporter for the Montreal Gazette. He later moved to settle in the United States. Moore’s fame springs from writing about exiled individuals. Fergus (1970) is one of his poignant novels that focus on delineating the artist –hero struggle with the mass media in self exile in the States .Moore believes that modern mass media can either be a means of creation or a weapon of self- destruction in any artist’s life , whether an actor ,a painter or a writer . In his novel Fergus , he focuses on delineating rather the negative impact of the life of publicity and mass media on the hero , who is a writer of an Irish descent like himself . He adopts the technique of presenting a hallucinatory kind of reality in which the actual world of the hero is inhabited by visiting ghosts of dead people from his past life in Ireland. Moore’s purpose in using this method is to highlight the readers understanding of true nature of the sacrifices that an artist makes for achieving his dream of living a celebrity figure in a place like America. Yet, Fergus’s predicament as an artist in exile is intertwined with Moore’s personal crises in Ireland .The novel becomes a medium for filtering his passion and nostalgia for his parents’ world, despite its stagnation and conflicting realities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".