A Systemic Functional Linguistic Analysis of Remote Real Estate Listings
Bibliographic record
Abstract
In the aftermath of the COVID-19 pandemic, the rural population in Canada increased faster than in any other G7 country. The province of Newfoundland was a distinct outlier, seeing the most drastic decrease of all rural Canadian regions at 6.4%. This was no surprise to local residents. Since the 1992 Cod Moratorium, these populations have faced continued socioeconomic hardship and decline. While decades of outmigration from rural Newfoundland to urban centres have been studied, current research does not account for this post-pandemic phenomenon. From a linguistic viewpoint, the population decline in Newfoundland has been redirected from regional discourse to a perpetuation of tourism development. While tourist discourse represents one strategy for combating population decline, the linguistic implications upon real estate listings are not known. This project investigated the linguistic implications of increased global mobility and urbanism on real estate listings. Drawing upon the theoretical framework of systemic functional linguistics (SFL), we analyzed the interpersonal, experiential, and textual metafunctions of a 2022 real estate listing from the remote town of Harbour Breton, Newfoundland. This project not only demonstrated the analytical value of SFL, but systematically revealed persuasive linguistic choices made by an author to attract buyers to a declining rural region. Findings include the frequent use of declarative mood structures, a prevalence of relational attributive processes, and major topical themes relating to the home. These features demonstrate a deliberate saturation of favourable details and a foregrounding of the features of the home above other notable factors (e.g., location, proximity, etc.).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".