Time thieves and space invaders: technology, work and the organization
Bibliographic record
Abstract
Purpose This paper aims to investigate the shifting boundaries between two experiential categories – home and work – for office workers. The boundaries are both spatial and temporal, and the paper seeks to analyse how certain kinds of mobile technology are being used in such a way as to make these boundaries increasingly permeable. Design/methodology/approach The research involved both the collection of quantitative data using a survey tool, and the gathering of qualitative data through in‐depth interviews. Findings The paper finds that the mobile technology discussed enables work extension – the ability to work outside the office, outside “normal” office hours. This provides flexibility with respect to the timing and location of work, and makes it easier to accommodate both work and family. But at the same time, of course, it also increases expectations: managers and colleagues alike expect staff to be almost always available to do work, which makes it easier for work to encroach on family time, and also leads to a greater workload. The ability to perform work extension is, then, a dual‐edged sword. Practical implications The paper provides both managers and non‐managers with insight into the effects of providing mobile technology to office workers, and suggests some mechanisms to mitigate negative effects. Originality/value The paper explores the impact of mobile technologies on non‐mobile office staff.
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 | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".