Fresh perspectives on the future of the office: A way forward
Bibliographic record
Abstract
The debate about the future of the office has taken on a life of its own as a result of pandemic-induced lockdowns. The viability and utility of the home/remote/anywhere working experiment at scale has opened up challenges and opportunities — so much so that the traditional ecosystem of commercial property investment is under challenge, together with the viability of the traditional office from both the suppliers’ and users’ perspective, and even the future of city centres is being evaluated. COVID-19 has shifted the focus to the people aspect of the equation and how and where work will be carried out, given the rise in importance of employee and community health and well-being. It has also highlighted the work-from-home versus living-at-work debate. This paper engages a broad range of diverse contributors with a wealth of experience and expertise in dealing with various aspects of the built, technological and workplace landscape, including the health, well-being, anthropological, behavioural change and sustainability factors. This wide-ranging holistic approach forms the basis for creating greater awareness and proposing frameworks and approaches within the corporate real estate (CRE) space to move forward.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".