Bibliographic record
Abstract
Few companies measure whether the design of their workspaces helps or hurts performance, but they should. The authors have collected data that capture individuals' interactions, communications, and location information. They've learned that face-to-face interactions are by far the most important activity in an office; creating chance encounters between knowledge workers, both inside and outside the organization, improves performance. The Norwegian telecom company Telenor was ahead of its time in 2003, when it incorporated "hot desking" (no assigned seats) and spaces that could easily be reconfigured for different tasks and evolving teams. The CEO credits the design of the offices with helping Telenor shift from a state-run monopoly to a competitive multinational carrier with 150 million subscribers. In another example, data collected at one pharmaceuticals company showed that when a salesperson increased interactions with coworkers on other teams by 10%, his or her sales increased by 10%. To get the sales staff running into colleagues from other departments, management shifted from one coffee machine for every six employees to one for every 120 and created a new large cafeteria for everyone. Sales rose by 20%, or $200 million, afterjust one quarter, quickly justifying the capital investment in the redesign.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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".