Common ground and the city : assumed community in Vancouver fiction and theatre
Bibliographic record
Abstract
This dissertation offers a new approach to an enduring question in literary studies: how do certain genres mediate an experience of “imagined community”? In studies of Canadian literature, texts are frequently analyzed for how they represent place—and how they evoke national, regional, local, or transnational communities by depicting characters’ lives in place. This project shares that interest in place, but rather than asking how place is represented, it asks what audiences are addressed when fiction and theatre performances refer to specific places. Shifting focus onto these works’ address to particular imagined audiences allows me to consider how they mediate their actual audiences’ relationships to specific places and to other local and non-local populations. Taking novels, short stories, and plays set in metropolitan Vancouver as a case study, I analyze narrative address using the tools of linguistic pragmatics, in particular theories of audience design, relevance, and common ground. I then adapt these ideas to the analysis of live performance in conventional theatres. I find a variety of different modes of address implicit in how these works style their references to the city and its landmarks. All of the plays and some of the narratives address audiences who share their knowledge of certain parts of the city. They offer insight into what parts of a city residents imagine sharing with their anonymous fellow city-dwellers, on what social basis they share these extended neighbourhoods, and what are the limits of this “common ground.” Other narrators address audiences for whom the city is unfamiliar territory. Their narratives illuminate the social contexts that connect people across spatial divides and the various interests that, in the narrators’ opinion, distant audiences might have in being introduced to Vancouver. While the written narratives address audiences who have a specific amount of knowledge of Vancouver but might themselves be anywhere, the plays potentially produce a “strong” form of common ground by bringing their audience together at a particular site. I argue that this experience constructs what Arjun Appadurai calls “locality,” thus offering insight into what locality might feel like in a modern Canadian city.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.046 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".