Bibliographic record
Abstract
An investor-subject critique is at the heart of criticisms of financial inclusion. This critique is driven by the idea that people are shifted from the security of the welfare state to the insecurity of financial markets. This chapter studies the case of housing. It does this because housing is arguably one of the most important investments made by investor-subjects and this case highlights the connections that exist between the financial system and other parts of the economy. Hofman and Aalbers (2019: 91; see also Jordà et al, 2014) comment that the: alleged dominance of finance is in many ways interdependent with real estate. For example: real estate has become the single largest form of collateral that banks provide credit on. Between 1870 and 2010, the share of mortgage loans in banks’ total lending portfolios doubled from 30 to 60 per cent in a group of 17 OECD countries including the US, Canada, Australia, Japan, the UK and 12 other European states. ... Finance may dominate the economy, but real estate finance dominates banking. Smith (2008) underlines the connection between housing and the financial system. Drawing on two qualitative studies done in the UK, she explores the conceptual links between housing, home and finance. Smith (2008) argues that ‘home’ has a variety of meanings. In the English-speaking world, she claims that the notion of home has become closely bound up with the idea of an investment. This means that the homeowner is seen to be an investor rather than a property-owning citizen.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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".