Bibliographic record
Abstract
The Northern Review 45 (2017): 57–75 https://doi.org/10.22584/nr45.2017.004This article focuses on the successful Swedish tradition in the field of innovation, but also discusses the flip side of an innovation culture that honours only radical innovation. Related to this tradition is a preference to measure innovativeness through patent data. Both these traditions imply a disadvantageous position for regions and companies located outside our large metropolitan areas. One problem relates to the interest in understanding how different degrees of innovativeness relate to different degrees of economic and social effects—a challenge that patent data, only to a very limited degree, addresses. This means that patent data disregards the fact that also incremental innovations “new to the region” or “new to the firm” might be powerful routes to a more dynamic development path, especially in more peripheral regions. To overcome such shortcomings, other measures and approaches are needed. One such approach developed and presented in this article is based upon Data Envelopment Analysis (DEA) and the Malmquist productivity index—employing Swedish longitudinal data, the article illustrates how they may be utilized to assess and make sense of regional technological innovation. Besides offering an unconventional picture of the regional innovation performance in Sweden, this methodological approach also identifies the northernmost part of Sweden (the Norrbotten region) as a region with its own path-breaking development trajectory. The article is concluded by discussing the region of Norrbotten as an example of a region that has traditionally capitalized on the exploitation and processing of natural resources and how such a region may diversify into new sectors using concepts such as related variety and smart specialization.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".