The changing context of knowledge-based work: consequences for comfort, satisfaction and productivity
Bibliographic record
Abstract
Developments in information and communication technologies permit a variety of forms of remote working. The ‘workplace’ now embraces a wide range of possibilities that extend beyond the domain of the ‘office’, reaching out to the home and to a host of public venue ‘hot-spots’ available within the city. This article examines the changing nature of the office workplace to understand the new and emerging spatial and temporal engagement of building inhabitants with their workplaces. It attempts to clarify the distinction between ‘individual’ and ‘shared’ experience of and engagement with manual and automated controls, and how these distinctions are manifest in a variety of different knowledge-based work contexts. It further illustrates how the emerging shift towards more mobile and transient workplaces requires rethinking the notions of a palpable inhabitant ownership, engagement and agency that are considered necessary to support higher degrees of satisfaction. The article concludes by presenting a framework that outlines the relationship between workplace technologies (environmental controls and information and communication technologies) and knowledge-based workers (both individual and groups) across various workplaces (home, office and city).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".