Strategies for using quantitative and qualitative metrics to optimise hybrid work solutions
Bibliographic record
Abstract
To address the emerging needs of the hybrid workforce, corporate real estate (CRE) organisations must adapt how they plan for, design and fit out workplaces. Every CRE group is still learning how to navigate this new environment, and there are no definitive solutions. This paper asserts, however, that a hybrid approach that is not well planned and carefully implemented will not meet the needs of employees or their organisations. A successful plan should be based on the right data about how an organisation’s employees are using space. Past methods of measuring space utilisation may not apply to the workplaces that support a hybrid work model. This paper describes how to take a phased approach to creating an effective hybrid workplace combining on-site and remote work. One of the challenges facing CRE is how to measure office utilisation and rebalancing existing spaces to accommodate evolving workstyles. This paper provides actionable advice on using the right quantitative and qualitative metrics to develop a hybrid work experience that will yield the best results. It also discusses the crucial roles of HR and IT groups in creating the optimum hybrid work solutions, as well as the importance of linking these efforts to the organisation’s business goals, unique organisational DNA and the needs of its people. Finally, the paper describes what makes Boston Consulting Group’s new Canadian headquarters in Toronto an example of a successful new post-pandemic work programme.
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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 | 0.273 | 0.337 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".