Conceptualising and measuring the location of work: Work location as a probability space
Bibliographic record
Abstract
There is currently considerable interest in workers performing tasks from a variety of workplaces, such as co-working spaces, transport-networks and cafés. However, it remains difficult to ascertain the extent to which this workplace mobility is altering urban economic geography, since most analyses of the location of economic activity in cities are based upon census-type data that assume a unique place of work for each worker. In this paper I propose a framework that extends the concept of place of work: work is probabilistically assigned to different types of workplace according to the proportion of work time spent in each. The limitations of census data are discussed and illustrated, after which the framework is operationalised in an exploratory survey. Census data suggest a modest increase in workplace mobility, with most work still taking place either at home or in a fixed workplace. The paper’s principal contribution is to explain these data’s limitations and show how work location can be operationalised as a probability space.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".