<i>Age of Iron</i> Adaptation and the Matter of Troy in Clements’s Indigenous Urban Drama
Bibliographic record
Abstract
In Age of Iron, Clements freely adapts Euripides’s Trojan Women. In Hutcheon’s terms, she “indigenizes,” drawing on the ancient play selectively and localizing it. Her localization is unusual because she creates a double setting and a palimpsest of Trojan and Indigenous referents. The action occurs in a place which is both contemporary Vancouver and ancient Troy; the fall of Troy is also the conquest of BC’s Aboriginal peoples. This duality allows Clements to lament historical suffering even as she insists that conquest is happening now on the streets of Vancouver. The superimposition of different frames of reference politicizes the gesture of adaptation: Clements’s transformative art implies that past patterns of oppression can be re-shaped. Yet adaptation of a classical text is an ironic gesture, given that the play stages the destructive imposition of European culture. The layered ironies of the play derive, as well, from Clements’s appropriation of the “matter of Troy.” Exposing the instability of national mythologies, she rejects the mythic descent of Britons from exiled Trojans, and claims Troy for her Aboriginal-identified characters. She revises British triumphalism, and instead of prophesying Troy reborn uses setting, set, and performance to dramatize the exilic present of “Trojans” in their homeland.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".