Monsters and near-death experiences in Eric McCormack’s First Blast of the Trumpet Against the Monstrous Regiment of Women
Bibliographic record
Abstract
Abstract Following Linda Hutcheon’s definition of parody as “repetition with a difference”, this essay exposes how a contemporary Canadian novel parodically responds to seminal Early Modern English pre-texts. Eric McCormack is not only a Canadian postmodernist (and postcolonial) writer born in Scotland but also a specialist in Early Modern English literature and thus an ideal representative of the intertextual situation of Canadian writing between literary tradition and the challenges of postmodern/postcolonial writing. The essay interprets McCormack’s sexual gothic novel First Blast of the Trumpet Against the Monstrous Regiment of Women discussing the way in which a literal or even literalist—rather than metaphorical—interpretation of literary and religious texts of the Early Modern period can make an important and sometimes harrowing difference in the lives of somewhat unsophisticated literary characters. McCormack’s ominously named character Andrew Halfnight literally interprets religious and literary texts he sees as signposts and guidelines of his personal behavior, thus showing how a literal interpretation of “canonical” texts limits the character’s ability to lead a self-determined happy life. The texts he refers to include the pamphlet The First Blast of the Trumpet Against the Monstrous Regiment of Women by Scottish reformer John Knox, Robert Burton’s Anatomy of Melancholy and John Milton’s Paradise Lost, and these subtexts are more than challenged through their intertextual transfer into erotic or perhaps even pornographic contexts which probably would have shocked the Early Modern authors (although, for example, Milton was at least not unwilling or unable to include eroticism in his work). Towards the end of the novel, the imagined reversal of the life-giving act of birth turns into a monstrous sexual act, which coincides with the protagonist’s near-death experience in an automobile accident on a snow-covered road in northern Ontario. This experience cum sexual act leads to the “un-birth” or “re-birth” of the novel’s main character into what he takes to be a happier and more fulfilled life in a paradise found or regained in Camberloo, Ontario.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.052 | 0.030 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".