Bibliographic record
Abstract
The Conservative governments of Margaret Thatcher and John Major introduced a range of initiatives aimed at breathing new life into the private rental housing market. These included one measure – housing investment trusts – that was specifically aimed at attracting investment in the market by financial institutions (DoE, 1995). Since coming to power in 1997, ‘New’ Labour has made clear that it too sees an important role for the private rental sector (PRS) and that it would welcome investment by financial institutions (DETR, 2000a). While there has been a modest revival in the size of the PRS over the past decade (see Chapter One), there has been very little investment in the sector by financial institutions. Indeed, financial institutions currently own a negligible amount of private rental housing. There are a number of reasons why they have made no significant investment in the sector, but one of them is the lack of market information about the private rental market. In particular, very little regular and reliable information has existed about residential rents and rates of return (Crook et al, 1995; Coopers and Lybrand, 1996). This situation contrasts with the extensive market information that is available about equities, gilt investment markets and a range of commercial property market indices (Morrell, 1991, 1995). Likewise, there are a number of respected indices for house prices, including those developed by the Halifax Bank and the Nationwide Building Society. If the financial institutions are to enter the PRS on a significant scale, market information about rent levels and rental yields is necessary to inform their investment decisions (Crook and Kemp, 1999).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".