Piloting: Adopting a prototype mindset for today’s workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is designed to help corporate real estate (CRE) leaders adopt a ‘learning by doing’ approach to workplace strategy and design to improve employee experiences and organisational outcomes. While the COVID-19 pandemic has brought lingering uncertainty relative to the future of the physical office, it has also spawned a spirit of openness to change and a willingness to experiment. In this paper, we discuss ways to learn by doing at various scales, with a focus on the largest-scale methodology: piloting. We show how pilots provide a low-risk approach to introduce flexible work policies and new ways of working within a sustainable financial model. We demonstrate that by generating data and insights, pilots can drive plans to scale and inform future space types within the larger real estate portfolio. This paper draws upon years of research and exploration conducted by MillerKnoll brands and our customers on pilots of varied types and scales. It provides a roadmap to implement a pilot, including questions to ask, selection criteria for teams, and locations and methodologies to measure success. Case studies illustrate various approaches and results achieved.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it