Den informella marknaden för hyresrättskontrakt i Sverige
Bibliographic record
Abstract
Sweden has a significant rental sector with about a third of the total number of households rented out. The intention of the rental market should be that it is accessible to all people in society, regardless of nationality or income. The general mediation of rental apartments today is through a queuing system, which in Stockholm shows that a queuing time can be up to half one's life. The alternatives to obtaining a tenancy will thus be an informal market with free price formation, more specifically illegal trading of tenancy contracts through transactions on a black market. First- and second-hand contracts are transferred to a counterparty in an unlawful manner, exchange of apartments or subletting in second-hand. The supply of rental apartments does not meet the demand that exists in the market and as people see opportunities to make money from an illegal trade, criminal activity is spreading. The aim of this thesis is to examine the informal rental market, its extent and problems and how the existence of the black rental market can contribute to the creation of potential winners and losers. The categorization of who is meant by winner and loser in that sense, can thus be clarified according to what households' overall housing preferences are. Winners with today's system can, given that the transactions are voluntary, be those who are involved in the transactions and those who are given opportunity to fulfill their preferences in the market. Conversely, losers are not given the opportunity to fulfill their preferences. The question that must therefore be addressed is how to identify what possible inputs there are to fulfill their preferences by the existence of the black market. The thesis also intends to study when it is considered that the black market is fair through two moral-philosophical orientations on justice, egalitarianism and libertarianism. The interpretations of what is considered fair, moral or ethical in the Swedish rental market are quite different. To clearly understand the crime, the essay will present the typical actors and what the typical forms of trading in tenancy contracts are. It will be discussed whether there are alternatives for those actors who act compared to those who choose not to act, as the acceptance of acting unethically and breaking the law has grown. If there are to be fewer winners and losers, measures must be taken to reduce the illegal trade in tenancies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.341 | 0.347 |
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".