Annual Population Survey Two-Year Longitudinal Dataset, January 2022 - December 2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The&nbsp;<i>Annual Population Survey</i>&nbsp;(APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the<a href="https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000026">&nbsp;</a><i><a href="https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=2000026">Labour Force Survey</a></i>&nbsp;(LFS), all its associated LFS boosts and the APS boost.</p><p>The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The&nbsp;APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. <a href="https://beta.ukdataservice.ac.uk/datacatalogue/series/series?id=200002#!/access-data">Person- and Household-level APS</a> datasets are also available.</p><p> </p><p>For further detailed information about methodology, users should consult the&nbsp;<i>Labour Force Survey User Guide</i>, included with the APS documentation.</p><p><strong>Occupation data for 2021 and 2022<br></strong>The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023:&nbsp;<a title="Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022" href="https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022</a><br></p><p></p>
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.250 |
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 it