Conceptual Metaphors, Geography, Literature, and the Implications on the In-place or Out-of-place of People and Actions
Bibliographic record
Abstract
There are three primary aims of this study: first, to investigate how geographical locations, countries of origins, building typologies, and vehicles/machines are presented as source domains frequently activated in metaphorical linguistic expressions to \npoint to entangled socio-economical, environmental, and political issues within Douglas Coupland's narratives. Second, to discuss the ways in which the author juggles what is coherent/incoherent with a mainstream spatialization or orientational metaphor; third, to ascertain if, beyond the author’s interest in “his geographical and historical surroundings” (McGill, 2000) and beyond his need to portray a subcultural logic of priorities, geographical metaphors allow Coupland to borrow labels of origin, both global and localised ones, and reshape them as a way of thinking that is socio-political and post-colonial in scope, questioning what is proper or inappropriate and for whom. A collection of evidence from different novels will be provided and analysed to demonstrate the expanded conceptual phenomenon Coupland generates from geographical metaphors “as a way of thinking and acting with geographical and political implications” (Cresswell, 1997).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".