Vested outsourcing in corporate real estate and facilities management
Bibliographic record
Abstract
In 2003 the University of Tennessee began a research project tasked to answer a simple question: ‘Is there a better way to outsource?’ Researchers studied some of the world’s most successful outsourcing relationships, including Procter & Gamble, Microsoft and McDonald’s. Researchers immediately saw trends in these successful relationships where organisations were shifting away from transaction-based agreements to collaborative outcome-based outsourcing relationships that the researchers described as a ‘vested’ mind-set. Researchers codified their learning into a methodology they coined ‘Vested Outsourcing®’, or Vested for short. Today, Vested is referred to as a mind-set, methodology and business model that enables highly collaborative relationships in which buying organisations and their service providers are committed equally to each other’s success. Organisations that have applied the concept often refer to it as a movement because of its power to transform the way organisations outsource. This article addresses the fundamentals of Vested Outsourcing as well as its applicability to corporate real estate and facilities management, including under what circumstances it can be most beneficial. Case studies from within Corporate Real Estate and Facilities Management (CREFM) are shared, including TD Bank, Vancouver Coastal Health and Novartis.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".