Textualizing History in Synge’s Riders to the Sea and O’Casey’s Juno and the Paycock: A Comparative Study
Bibliographic record
Abstract
In a radical reaction against the idealism and sentimentality of melodrama, a few dramatists in the second half of the nineteenth century shifted the dramaturgy style into what came to be known as realism. This school of thought emphasizes the presentation of life as it is without exaggeration, illusions or artifices. It is evidently reflected in the dramatic works of some playwrights like John Millington Synge and Sean O’Casey who are the main concern of this paper. In this respect, the textualization of history is significantly an important aspect of realist plays. Therefore, this comparative study explores the textualization of history in two iconic Irish plays; John Millington Synge’s Riders to the Sea (1904) and Sean O’Casey’s Juno and the Paycock (1924). These plays are realistic portrayals of Irish society and the profound impact of historical events on the lives of ordinary individuals. By utilizing a new historicist and postcolonial reading, this study aims to uncover how historical events are recorded, reinterpreted, and recreated within literary works. It investigates the ways in which Synge and O’Casey incorporate these historical elements into their plays, demonstrating the dual nature of the relationship between history and literature. Besides, the study will conclude by proving the indirect commitment of these playwrights to their nations, countering accusations leveled against them.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".