MétaCan
Menu
Retour à la cohorte
Enregistrement W6967152787 · doi:10.5255/ukda-sn-7908-12

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

2022· dataset· en· W6967152787 sur OpenAlexaboutno aff

Notice bibliographique

RevueUK Data Archive · 2022
Typedataset
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésQuarter (Canadian coin)UnemploymentCurrent Population SurveyPopulationSurvey data collectionSample (material)Work (physics)Population projectionSurvey methodology

Résumé

récupéré en direct d'OpenAlex

Background 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. New reweighting policy Following the new reweighting policy 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. Longitudinal data 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. Secure Access data 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). Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: day, month and year of birth standard occupational classification (SOC) relating to second job, job made redundant from, last job, apprenticeships and occupation one year ago five digit industry subclass relating to main job, last job, second job and job one year ago These extra variables are not available for every quarter or dataset. Users are advised to consult the 'LFS Variable Catalogue' file available in the Documentation section below for further information. Variables DISEA and LNGLST 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. An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk. Latest edition information For the twelfth edition (January 2022), a data file for April 2021 - September 2021 has been added. String COVID-19 variables have been removed. The documentation has also been updated.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,111
Score d'incertitude au seuil0,371

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,014
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,008
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,1110,117

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,086
Tête enseignante GPT0,367
Écart entre enseignants0,282 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUK Data ArchiveTravaux en français237 207