"Our Language is the Forest": Landscapes of the Mother Tongue in David Greig's <em>Dunsinane</em>
Bibliographic record
Abstract
Tracing early visits to and excavations of Dunsinane Hill in Perthshire, Scotland, this essay argues that Shakespeare and the overpowering legacy of his "Scottish play" have left an imprint that is both ecological and ideological. Situated within broader conversations about cultural heritage, literary tourism, colonialism, and nationalism, my analysis of Shakespeare's indelible mark on Dunsinane Hill—as a place and an idea—provides a theoretical and literal groundwork for understanding how Scottish playwright David Greig activates the territorial lexicon of appropriation in his 2010 play Dunsinane. For Greig, the act of appropriation is not just about speaking back to Shakespeare but about doing so on land that was never his and in a language that he never understood in the first place. I show how Greig concentrates the power of his speculative sequel in and around the figure of Gruach (the historical Lady Macbeth), who not only embodies the deeply gendered relationship between language and landscape but also reclaims that relationship in order to critique the longstanding and devastating colonial conflation of women's bodies, mother tongues, and the land itself.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".