The Development of the Outsourced Facility Service Market in the EU
Bibliographic record
Abstract
The Facility Service (FS) industry lacks comprehensive scientific reports due to its fragmented representation in the NACE industry classification system in the EU. This article addresses this gap by offering insights into the FS market's turnover, number of companies, and employees. The study examines the development of turnover, company count, and employee numbers in the outsourced FS sector in the EU and the five major European economies since 2008. The European norm EN 15221:4 defines FS activities, which were cross-referenced with Eurostat's economic data to identify relevant FS services. Extensive data quality checks were conducted to ensure accuracy. From 2008 to 2018, the FS sector's size in terms of turnover and companies was estimated and compared across countries. Findings reveal that the EU's FS sector reached a turnover of over 1142 billion in 2017, with nearly 2 million companies and over 15 million employees. Despite the 2008/2009 financial crisis, the FS sector in the EU demonstrated stability compared to the broader economy in the selected countries.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".