Exploiting the life course: Life course segmentation in the Brussels co-living sector
Bibliographic record
Abstract
Co-living’s strong growth in recent years has attracted increasing attention from the media, the real estate industry and public authorities. We argue that co-living is not radically new in terms of its design and services, which are similar to other ‘beds sectors’, but rather in terms of its target audience, young professionals. To understand this discrepancy between its innovative image and commonplace characteristics, we develop the concept of life course segmentation – a form of class monopoly rent – which highlights the mechanisms underlying the expansion of ‘beds sectors’ and ‘total-life landlordism’. Based on an in-depth qualitative study of the Brussels co-living sector, we demonstrate how the realization of class monopoly rent in co-living relies on the segmentation of the housing market according to the life course, through the promotion of distinct lifestyles associated with specific housing types. Co-living companies take advantage of the constraints facing young professionals in the housing market to discursively position co-living as an exclusive product uniquely suited to addressing these constraints. Recent developments in co-living, now targeting older adults, further this process of age segmentation by allowing the sector to extend its potential customer base while maintaining high prices thanks to the creation of age-specific class monopoly rents. This case demonstrates how the real estate industry exploits the life course in pursuit of rent. Total-life landlords not only seek to capture rents across the life course but also seek to extract greater rent from each life course stage by deepening the segmentation between them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".