Parsing Perceptions of Place: Locative and Textual Representations of Place Émilie-Gamelin on Twitter
Bibliographic record
Abstract
We increasingly engage in geographies mediated by social media, which is changing how we experience and produce places. This raises questions about how ‘place’ is conceived and received in networked virtual spaces. Place has remained difficult to grasp in both geography and communications studies that utilize social media data. To attend to this, I first develop a conceptual framework that bridges the phenomenology of spatiality with the communication of place. I then present a case study of Place Émilie-Gamelin in Montreal: a plaza located atop the city’s busiest transit hub. Despite its geographic centrality, it is a liminal space appropriated by marginalized groups and contentious political movements. Since 2015, it has been subject to a city-led revitalization program with intentions of attracting party-goers and tourists. Using a communications geography framework, I collected a year’s worth of tweets, first, employing a filter to capture georeferenced tweets in and around the study site, and second, using the site’s toponyms to retrieve tweets through textual queries. To understand these representations, I coded them by relevance, theme and communicative function. Results showed a place evolving in scope, name and meaning, reflecting diverging flows and uses. I found that there were more textual connotations of the study site than there were geotweets, and that the former were more diverse in their representation of place. The thesis demonstrates how promotional content on Twitter should be more critically analyzed in concert with expressive and descriptive tweets and geotweets, and that this implies spatial ontologies and data collection methods that consider a place on social media as a discursive construction. This is especially so since Twitter has become increasingly ‘platial’ through internal changes and its entwinement with other social media platforms: changes which require consideration in all Twitter-based spatial and textual analyses. The study provides an updated perspective on Twitter’s use in the spatial humanities, GIScience and geography and contributes to those interested in applying more nuanced cartographies of places.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".