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Record W2189152684

UK's output lags in retail and financial services

2005· article· en· W2189152684 on OpenAlexaboutno aff
Gabriel Rozenberg, Economics Reporter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGerman Economic Analysis & Policies
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityBusinessGovernment (linguistics)Quarter (Canadian coin)Agency (philosophy)PaceFinanceAgricultural economicsEconomicsEconomic growthGeography
DOInot available

Abstract

fetched live from OpenAlex

countries. An adviser from the Sector Skills Development Agency (SSDA), the Government taskforce for improving skills levels across the economy, said that there were possibly too many managers in the financial Vicki Belt, a senior research adviser at the SSDA, said: There’s a negative relationship between levels of productivity and the amount of managers. There could be too many managers in the financial services sector. Retailers were not attracting enough graduates, she said. Productivity, which is measured as output per person, grew at an annual pace of just 0.3 per cent in the second quarter this year, the weakest since 1993. By contrast, in America output per hour grew by an average of 2.5 per cent a year between 1995 and 2004. Studies have put a large part of America’s strong productivity performance down to an ITdriven surge in efficiency in retailing. A study by the Centre for Economic Performance at the London School of Economics suggested that 80 per cent of the performance gap was down to the effectiveness with which IT was used. However, the SSDA highlighted Britain’s excellent performance in those sectors, where the country came at or close to the top of the league table. Britain’s productivity levels in the manufacture of food, drink and tobacco were ranked second only to Canada. It also had the highest level of productivity in the manufacture of furniture, jewellery, musical instruments and toys.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.018
GPT teacher head0.191
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2005
Admission routes1
Has abstractyes

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