Autonomy and control in the (home) office: Finance professionals' attitudes toward working from home in Canada as a result of COVID‐19 lockdowns
Bibliographic record
Abstract
Abstract This paper explores the shift to working from home among finance professionals in Canada as a result of the COVID‐19 pandemic. We present the results of a survey that invited quantitative and qualitative responses about attitudes toward working from home, the overlap between paid and unpaid (i.e., childcare and other caregiving) work in the home, changing relationships with employers, and preferences regarding the organisation and location of work. We argue that enforced working from home signalled a shift in outlook among finance professionals that, beyond stated preferences to work from home, shows both that many are seeking more autonomy and control over their working lives and a distinct ambivalence about working from home. This is significant in sectors like finance where overwork is common and in‐office dynamics are seen, especially by managers and employers, as particularly important in relation to mentorship, advancement, and promotion, often within rigid masculinist hierarchies. Thus, an eventual return to ‘normal’, i.e., full‐time office‐based work, may be especially appealing in this sector. This paper contributes to the expanding literature on working from home resulting from COVID‐19 lockdowns in white‐collar professions within and outside of geography, with a focus on the literatures on work, workplaces, and social reproduction in economic and financial geography.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".