Office Places, Work Spaces: An Ethnographic Engagement with the Spatial Dimensions of Work
Bibliographic record
Abstract
Have you ever been curious to know more about how people engage with their place of work? This article explores the spaces and places of a scientist’s academic office. It draws on four weeks of in-depth participant observation, interviews and visual analysis at the University of Cape Town to create an in-depth understanding on how the office, as a thing, shapes behaviour. Theoretically, this paper draws on phenomenological thought, Henri Lefebvre’s (1991) theory on the social production of space, and Tim Ingold’s (2000) ideas on the ‘taskscape’ to analyse the spatial components of work within and beyond the academic office. It argues that the office is far more intricate than just the site of non-manual labour. Indeed, there appears to be a unique way in which the performance of one’s academic discipline disciplines space.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".