Putting public value at the heart of cultural excellence: Exploring a transformational journey of workplace strategy at the BBC
Bibliographic record
Abstract
The arrival of a new Director General and Chairman of the BBC Trust in 2004, together with the eighth BBC Charter, which came into operation from 1st January, 2007, heralded a new era for the BBC and prompted a fresh approach to the property portfolio. The Charter, which runs for ten years, contained a number of new commitments that aimed to redefine the Corporation's role as a public service provider. BBC Workplace is playing a pivotal role in supporting the BBC's efforts to deliver public value. This paper outlines the transformational journey taken by BBC Property/BBC Workplace to develop an intelligent real estate strategy that empowers the Corporation to deliver high-quality, original content, drive innovation, support challenging new technologies and engage a global audience. Today, the BBC's real estate portfolio comprises 207 properties and 571,000m2 of space across the UK. Over the last decade, the BBC Property/BBC Workplace teams have focused on an intense programme of portfolio optimisation to deliver the Corporation's vision of ‘public value’, while ensuring flexibility and innovation to attract and retain the best creative talent. This paper will explore lessons learned from four milestone projects in the BBC Property/BBC Workplace journey towards a new, more intelligent, real estate strategy.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.046 |
| Scholarly communication | 0.063 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".