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Record W2116284840 · doi:10.1787/882376471514

Measuring the Interaction Between Manufacturing and Services

2005· report· en· W2116284840 on OpenAlexaboutno aff
Dirk Pilat, Anita Wölfl

Bibliographic record

VenueOECD science, technology and industry working papers · 2005
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessDiversification (marketing strategy)ManufacturingManufacturing sectorTertiary sector of the economyProduction (economics)Industrial organizationService (business)MarketingLabour economicsEconomics

Abstract

fetched live from OpenAlex

This paper examines the interaction between services and manufacturing using several types of data and shows that the distinction between manufacturing and services is blurring. Services make important contributions to production, mainly through their direct contribution to total output and final demand, but to some degree also through their indirect contribution via other industries. However, services are more independent from other industries than the manufacturing sector. Most inputs that are necessary to produce services are derived from the services sector itself. Moreover, their role as providers of intermediate inputs to other industries is not yet as strong as that of the manufacturing sector. The paper also shows that a growing share of workers in the manufacturing sector is engaged in services-related occupations. Using a broad definition of service-related workers, up to 50% of manufacturing workers are in such occupations. Using firm-level data the paper finds that, despite anecdotal evidence on a growing share of services turnover within the manufacturing sector, manufacturing enterprises in most countries are not very diversified in their constituting establishment, i.e. they do not have many establishments engaged in services production. Canada is a notable exception in this respect. In other countries, it is likely that diversification primarily occurs at the level of the enterprise group. On the other hand, data on products suggest that manufacturing firms and establishments appear to derive a growing share of turnover from services, notably in countries such as Finland and Sweden.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.097
GPT teacher head0.250
Teacher spread0.154 · 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
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

Citations195
Published2005
Admission routes1
Has abstractyes

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