Strategic spatial essentialism: Latin Americans' real and imagined geographies of belonging in Toronto
Bibliographic record
Abstract
This paper contributes to debates on the empirical and conceptual potentials of anti-essentializing notions such as ‘thirdspace’ with the aim to open new epistemological and political grounds. Based on the findings of ethnographic research, I critically examine two spatial strategies (the deliberate creation of an ethnic neighbourhood, and the securing of a community centre) that Latin American immigrants in Toronto, Canada, developed to appropriate urban space and lay claims to equal rights. The case of Latin Americans' struggle for belonging in Toronto serves to reflect on how and why new immigrant groups today (re)construct collective identity spatially. I argue that immigrants strategically essentialize their identities in and through place in order to make themselves visible and their voices heard. Ethnic places represent sites of resistance and creation where immigrants construct their own subjectivities while also redefining dominant notions of inclusion and citizenship. Although locally grounded, these new immigrant identities remain fluid and engage with multiple forms of exclusion [The] situation is simply sad; the [Latin American] community … is one of the most orphan communities … in [Toronto] … [We] don't even have a place where to dig our own grave basically. If there is need to get together … a meeting … there is no place. We have to be looking for a basement … for a recreational centre to give us a room … If there is a social or cultural event, we do not have a place where … we can present what we have … [It] is sad and it is a reality. (Cesar Palacio, city councillor candidate to Toronto's 2003 municipal elections, interview, 2 May 2003, translated from Spanish)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".