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Record W6892496317 · doi:10.5255/ukda-sn-7908-13

Labour Force Survey Two-Quarter Longitudinal Dataset, April 2001 - December 2021: Secure Access

2022· dataset· en· W6892496317 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUK Data Archive · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)UnemploymentPopulationCurrent Population SurveySurvey data collectionSample (material)Work (physics)Population projectionSurvey methodology

Abstract

fetched live from OpenAlex

<b>Background</b><br> The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.<br><br><span style="font-weight: bold;">New reweighting policy</span><br>Following the <a href="http://doc.ukdataservice.ac.uk/doc/8343/mrdoc/pdf/biennial_lfs_aps_reweighting_policy.pdf" target="_blank" style="background-color: rgb(255, 255, 255);">new reweighting policy</a><a href="http://doc.ukdataservice.ac.uk/doc/8343/mrdoc/pdf/biennial_lfs_aps_reweighting_policy.pdf" target="_blank" style="background-color: rgb(255, 255, 255);"></a> ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.<br> <br> <b>Longitudinal data</b><br> The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.<br> <br> <b>Secure Access data</b><br> Secure Access longitudinal datasets for the LFS are available for two-quarters (SN 7908) and five-quarters (SN 7909). The two-quarter datasets are available from April 2001 and the five-quarter datasets are available from June 2010. The Secure Access versions include additional, detailed variables not included in the standard 'End User Licence' (EUL) longitudinal datasets (see under GNs 33315 and 33316).<br> <br> Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to:<ul><li>day, month and year of birth</li><li>standard occupational classification (SOC) relating to second job, job made redundant from, last job, apprenticeships and occupation one year ago</li><li>five digit industry subclass relating to main job, last job, second job and job one year ago</li></ul>These extra variables are not available for every quarter or dataset. Users are advised to consult the 'LFS Variable Catalogue' file available in the <i>Documentation</i> section below for further information.<br> <br> <b>Variables DISEA and LNGLST</b><br> Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018. <br> <br> An article explaining the quality assurance investigations that have been conducted so far is available on the <a href="https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/methodologies/analysisofthediscontinuityinthelabourforcesurveydisabilitydataapriltojune2017tojulytoseptember2017" target="_blank">ONS Methodology</a> webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.<br> <br> <b>Latest edition information</b><br>For the thirteenth edition (June 2022), a new data file for July-December 2021 has been added to the study. The data files for January 2020- September 2021 have been re-deposited and updated. The documentation has also been updated.<br>

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0220.034
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.2920.019

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.

Opus teacher head0.084
GPT teacher head0.366
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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