Quarterly Acquisitions and Disposals of Capital Assets Survey, 2015-2016: Secure Access
Bibliographic record
Abstract
The <i>Quarterly Acquisitions and Disposals of Capital Assets Survey (QCAS)</i> is the new name for a more detailed <i>Quarterly Survey of Capital Expenditure</i> (available from the UK Data Archive under Secure Access conditions from SN 6708 (1998-2014). From quarter one 2015, this new survey will collect the value of capital assets bought and sold, which is important information for the UK National Accounts. It is a statutory survey with a stratified random sample of approximately 27,000 businesses selected from the Inter-Departmental Business Register, where the strata are defined by Standard Industrial Classification (SIC) 2007 and employment size of the business.<br> <br> Results from the survey provide essential information for the UK National Accounts and feed into the compilation of Gross Fixed Capital Formation (GFCF). GFCF is a major component of the expenditure measure of UK Gross Domestic Product (GDP), which shows the total economic activity taking place in the country. It is used to monitor the UK's financial health and prosperity over time and in comparison to other countries.<br> <br> The main reasons for the changes to the survey are to move to the updated European System of Accounts (ESA) 2010 manual, the international guidance for national accounts. Several presentational issues have been addressed but the main changes to the survey are:<ol><li>new questions have been added (increase from 11 to around 30) to improve the quality of estimates and to meet the latest European legislation requirements (ESA 2010) </li><li>the lower limit of £500 for the value of reported assets has been removed, so all relevant assets (even those below businesses’ Asset Register threshold) should be reported </li><li>small tools used in production have been included within the definition of GFCF</li><li>improvements have been made to the questionnaire’s layout with new sections and headings, designed to make completing the questionnaire easier </li></ol>The data from the new questions will not be included in estimates of GFCF and its components until 2017, when 2 years of data will be available for quality assurance.<br> <br> <I>Linking to other business studies</I><br> These data contain Inter-Departmental Business Register 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.<br> <br> For the second edition (February 2017), data for quarters 3 and 4 of 2015 and quarter 1 of 2016 have been added.<br> <br>
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".