Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".