Platform-mediated territorial stigmatization and destigmatization: Unpacking Reddit discussions on moving to “the other” London
Bibliographic record
Abstract
This paper interrogates the role of digital platforms in advancing territorial stigma. Drawing from research that has discerned the channels with which territorial stigma has been produced and mediated through — commonly referred to as ‘modes’ — I argue digital platforms are a mode in their own right. Digital platforms are on par with, and may have already surpassed, pre-platform media modes (e.g., newspapers, television) in terms of their use for forming preconceptions of cities in lieu of lived experience. Accordingly, this study investigates discussions on the social networking platform Reddit, about moving to the mid-sized city of London, Ontario in Canada. I use a hybrid thematic analysis to identify (de)stigmatizing content within a topic-based forum (a subreddit). The results show that the platform mediated its users’ reproduction of territorial stigmas stemming from pre-platform modes, rather than having generated entirely novel stigmas. Moreover, many users enacted or reworked established territorial destigmatization strategies to contest, counter, or cope with London’s territorial stigma. This paper contributes to territorial stigma research by examining a lesser-known mid-sized city, which in turn expands the current understanding of stigmatization in Canada hitherto limited to its materialization in large cities. By demonstrating Reddit’s modal potentiality, the paper provides the necessary empirical basis for future studies interested in examining other digital platforms through the theoretical framework of territorial stigma. Furthermore, it encourages future conceptualizations of the media mode of territorial stigma production to include digital platforms alongside traditional and mass media.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 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".