The Exaggerated Reports of Offices’ Demise: The Strength of Weak Workplace Ties
Bibliographic record
Abstract
In mid-March 2020, Canadian society pivoted from business-as-normal to lockdown and social distancing. By the end of March, 39 percent of Canada’s workforce was working from home (Deng et al, 2020), leading some to declare that ‘this might just be the end of the office as we knew it’ (Vasel, 2020). Indeed, the percentage mentioned in Deng et al’s report appears large, especially if one assumes the number was close to 0 percent before the pandemic: but such an assumption would be inaccurate. There has been a slow but steady increase in remote work (from home, but also cafés, co-working spaces, cars, and so on) over the last 30 years (Felstead and Henseke, 2017; Ojala and Pyöriä, 2018; Putri and Shearmur, 2020). Depending on how it is estimated, about 20 to 30 percent of the workforce did not regularly work in a ‘usual place of work’ pre-pandemic. Furthermore, it had become common for people officially assigned to a usual place of work – such as an office – to work part of the time (typically one day a week) from home (Ojala and Pyöriä, 2018; Shearmur, 2020). In this chapter, we suggest that working from home will become more common, but that offices will remain relevant. There are two related reasons for this. First, working away from the office was already common for many office workers, without offices disappearing: rather, office space has been evolving (often towards shared spaces), and this will continue as businesses become more familiar with remote work – the pandemic did not start this trend.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".