Towards a post-COVID geography of economic activity: Using probability spaces to decipher Montreal’s changing workscapes
Bibliographic record
Abstract
In March 2020, many workers were suddenly forced to work from home. This brought into stark relief the fact that urban economic activity is no longer attached to specific workplaces. This detachment has been analysed in research on organisations and workers, but has not yet been incorporated into concepts used to document and plan the economic geography of cities. In this article, three questions are explored by way of an original survey: first, how can a shift in the location of economic activity be measured at the urban scale whilst incorporating the idea that work is not attached to a single location? Second, what is the nature of the shift that occurred in March 2020? Third, what does this tell us about concepts that have underpinned the study of urban economic form by geographers and planners? Applying concepts developed in organisation studies and sociology, we operationalise the idea that economic activity happens across multiple spaces: it occurs within a probability space, and since March 2020 it has shifted within this space. To better understand and interpret the longer-term impact of this shift on cities - downtowns in particular - we draw upon interviews with people working from home.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".