Reshaping essential public spaces and services: towards socio-spatial justice in a post-pandemic era
Bibliographic record
Abstract
In our last viewpoint article, we illustrated the hidden stories of immigrant suburbs during the COVID-19 pandemic and challenges facing racialised communities. This article delves deeper into intensified social and spatial inequalities by interrogating: what are the ‘essential’ public spaces, places and services that must remain accessible to benefit the settlement, well-being and inclusion of marginalised, racialised, immigrant populations? What engagement approaches effectively include racialised minority groups’ voices in decisions about the future of public space and (sub)urban systems? We emphasise the utility of a socio-spatial justice framework in reimagining and reconfiguring essential public spaces and associated services in the aftermath of the COVID-19 pandemic. Using examples from the Canadian context (i.e. community land trusts, cultural district plans, cultural festivals and food systems), we unpack pillars of distributional, procedural and recognitional justice to interrogate the status quo and illuminate pathways to more inclusive, fair and accessible communities. This article was published open access under a CC BY licence: https://creativecommons.org/licenses/by/4.0/ .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".