Gentrification, resistance, and the reconceptualization of community through place-based social media: the future will not be Instagrammed
Bibliographic record
Abstract
Parkdale is a neighbourhood in Toronto, Ontario that has experienced tensions regarding who has a ‘right’ to the community. ‘@ParkdaleLife’ developed as a place-based social media presence that centred the lived experiences of the community and its changing urban landscape by ‘meme-ifying’ community realities. It earned a substantial following by sharing often-satirical snapshots that depicted community life, critiqued gentrification, and mobilized resources to meet community needs (e.g., through the food bank, land trust, legal clinic). We argue that @ParkdaleLife’s effective bridge-building between online and offline communities may indicate as-yet under-studied opportunities for leveraging social media in contemporary urban social movements. We use media content and auto-ethnographic vignettes to explore @ParkdaleLife’s use of humour, anonymity, and digital media to construct place and community as a mechanism for confronting the complex forces of gentrification. The resulting analysis produces critical new perspectives for exploring the role of place and technology in increasingly digitally mediated urban social movements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".