Annual Population Survey Two-Year Longitudinal Dataset, January 2020 - December 2021
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.<br></p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.001 |
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".