Acoustical Design Guide for Open Offices
Bibliographic record
Abstract
This design guide was prepared as part of a research project funded by PWGSC to investigate speech privacy in open-plan offices. Two major types of work area in open offices are currently in vogue:· cubicles where individual workstations are delineated by barriers and· the more open 'team-style' where groups of workers have unrestricted visual access among themselves but are shielded from adjacent work areas by barriers.Within the team-style work area, where sound paths are usually quite unobstructed, speech can be very intrusive. Since team-style work areas are usually separated from each other by fairly high barriers, offices incorporating this type of work area have the same problems with intrusive speech between work areas as found in offices having mainly cubicles. Thus, this guide gives, without detailed explanation, sets of recommendations to reduce the intrusiveness of speech in both types of office. (More information is available in the appendix and related reports.) Criteria are first given for reducing speech intrusion between cubicles because the same factors are important when considering sound transmission between team-style work areas. The problems specific to sound transmission within team-style areas are then dealt with.
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.226 | 0.127 |
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".