MQ5: Investment by Insurance Companies, Pension Funds and Trusts, 1997-2023: Secure Access
Bibliographic record
Abstract
<p>The <i>MQ5</i> survey includes quarterly net investment data arising from financial transactions (investments) made by insurance companies, self-administered pension funds, investment trusts, unit trusts and property unit trusts. Annual balance sheet data for short-term assets and liabilities are also reported. Income and expenditure data for insurance companies and self-administered pension funds are reported quarterly.<br> <br> <i>Linking to other business studies</i><br> These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research (in particular, users interested in geographical variables for <i>MQ5</i> may also consider applying for the <i>Business Structure Database</i> (SN 6697) and obtain them via linking).<br> <br><span style="font-style: italic;">Latest edition information</span><br></p><p class="MsoNormal">For the 12th edition (November 2023), 28 quarterly data files for 2022 and 7 files for Quarter 1, 2023 have been replaced. Seven new data files for Quarter 2, 2023 have been added to sections 101, 102, 103, 104, 118, 120 and 123. One Excel document has also been updated.<o:p></o:p></p>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.011 |
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; both teacher heads agree on what is shown here.
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".