The Tetris City: Workplace mobility and the dynamic spatiality of knowledge work in Silicon Valley North, Canada
Bibliographic record
Abstract
Economic geography scholarship rests on the assumption that work has a well-defined and fixed location. Existing data and traditional methods for capturing the location of work have not yet been able to grasp the increasingly dynamic spatiality of knowledge work that other disciplines — like Mobilities and Management and Organizational Studies — have been discussing for some time. These disciplines suggest that as a result of greater worker mobility, flexibility and digitization work has been extending beyond the official, well-defined workplace. If this is the case, the concepts that underpin our understanding of where work takes place need to be rethought. The dominant paradigms that shape how economic geographers and urban planners think about the location of work are informed by fixed categories, which likewise treat places and workers as fixed in time and space. And while recent studies using census-type data are showing a modest but steady rise in mobile work, they fail to capture the locations (in addition to the formal workplace) that are used for work throughout the day, the week, the month and the year. At present, this complexity can only be qualitatively explored. A closer look at these nuances will improve our understanding of new ways of working, how spaces are being used for work, and how these changes affect real estate and urban planning. This is especially important given the Covid-19 crisis and the unprecedented shift to remote work. The experiences of knowledge workers (for some of whom the “new normal” has been normal for some time) pre-pandemic reveal valuable insights on what workplaces are likely to be like once the pandemic has been resolved. Indeed, focusing on knowledge workers in Canada’s high-tech and start-up hotbed in Kitchener, Cambridge and Waterloo, this dissertation confirms that work has been extending beyond the formal, designated workplace (i.e., the office) to include a number of other locations. What is more, it reveals that the increasingly fuzzy boundaries between work, life and play have produced fuzzy definitions of work and the workplace. Meanwhile, interviews with corporate consultants and real estate professionals reveal that firms have been changing their offices to mirror new trends, and while some have been reducing the amount of space required per worker to generate more collaborative and attractive work environments, others have been deploying the same strategies (i.e., flexwork) for cost-saving purposes. No matter the motive, the willingness to pay a premium for flexible spaces (and flexible leases) has exacerbated office real estate costs, driving more companies to pursue flexwork options, and thereby intensifying the need for workplace mobility. This circular relationship between flexwork and rising office rents also makes it difficult to keep track of who is using spaces, how and for how long. Interviews with city planners reveal that while they are cognizant of these changes, they feel limited in their capacity to regulate the real estate market. Finally, flexwork has become, above all, a real estate play and a feature of the financialized and deregulated real estate market. What is more, because these new ways of working are considered innovative and creative, they’ve garnered institutional support, thereby obscuring their downsides. I conclude that workplace mobility affects the city in a manner resembling the “Tetris Effect”, or the need to constantly think about and adapt space — across personal and professional domains — in order to maximize economic utility. Neoliberal planning, with its focus on growth, neglects the downsides of workplace mobility as it seeks out ways to accommodate it. This calls into question the effectiveness of planning tools (as well as their ideological foundations) in ensuring that corporate decisions are in the interest of the public in the longer term
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".