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Record W6892393083 · doi:10.5255/ukda-sn-6702-20

Monthly Wages and Salaries Survey, 2000-2021: Secure Access

2021· dataset· en· W6892393083 on OpenAlexaboutno aff

Bibliographic record

VenueUK Data Archive · 2021
Typedataset
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsIndex (typography)Quarter (Canadian coin)Stratified samplingCover (algebra)Key (lock)Business information

Abstract

fetched live from OpenAlex

<p>The <i>Monthly Wages and Salaries Survey</i> (MWSS) is the main source of information for three key indicators of Short-Term Earnings generated by the Office for National Statistics: the Average Earnings Index, the Average Weekly Earnings and the Index of Labour Costs per Hour.<br> <br>The MWSS is distributed monthly to approximately 8,800 businesses and covers around 12.8 million employees. Companies are required to respond under the Statistics of Trade Act 1947. Businesses are selected from the Inter-Departmental Business Register. Every company with more than 1,000 employees is surveyed. Sampling is random for businesses with fewer than 1,000 employees. The MWSS does not cover businesses with fewer than 20 employees, and so the very smallest businesses in the economy are not represented. The self-employed and government-supported trainees are also not surveyed.<br> <br>The major strength of the MWSS is that it provides comprehensive information on earnings, by industry. In terms of industrial coverage, information on all industries is collected, as defined by the Standard Industrial Classifications (1992). Information on both the public and private sectors is available.<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.</p><p><br><span style="font-style: italic;">Latest edition information</span><br>For the twentieth edition (June 2021), a new monthly data file for February<span style="color: rgb(51, 51, 51); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"><span> </span></span>2021 has been added to the study.<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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.267
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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