Bibliographic record
Abstract
This review of the innovation literature seeks to identify the role of skilled labor in the process of innovation and technological change.After an introduction of main innovation theories, the role of skills is analyzed from several perspectives: (1) Independent innovator -entrepreneur; skills deployed and needed; the role of education (2) irm -the contribution of skilled labor to innovation from within the firm and from external sources.(3) Regional systems of innovation -Endowment of regions and cities in human resources, regional/local labour markets and knowledge spillovers (4) National systems of Innovation-national institutions and policies regarding human resources, labour markets, education system and various aspects of economic and technological infrastructure.( 5) Technological milieu.skilledlabor involved in innovation evolves in various environments such as scientific, technical and trade associations, formal and informal contacts.(6) Scientific base.-Therole of industry-university and public-private research collaboration in innovation.(7) Is innovation skill-biased?The second part of the study looks at findings of recent studies of innovation and technology adoption in Canadian manufacturing and services with regard to skilled labor.Also addressed is the impact of innovation on skills.The shortage of skilled labor is widely recognised as an obstacle to innovation and adoption new technologies, especially by firms that introduce the most original innovations and the most advanced technologies.Overall, the innovation literature offers little in terms of concrete general information on particular skills needed for successful innovation.The paper concludes with a critical assessment of shortcomings of innovation and related surveys with regard to information on skilled labor and its role in innovation and technology adoption.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".