Knowledge economies: innovation, organization and location * Wilfred Dolfsma
Bibliographic record
Abstract
This book discusses the nature of knowledge, knowledge development and knowledge diffusion—topics that should be of interest to economic geographers. It seeks to show that knowledge and information are the key resources behind the dynamics of an economy. However, since he is both an economist and philosopher, Dolfsma undertakes these issues from a slightly different perspective than economic geographers might be used to. In order to understand the knowledge economy, Dolfsma draws from a diverse set of theoretical traditions and approaches: political science, sociology, history and economic geography to mention a few. That being said, this book is still firmly anchored in a relatively traditional (neoclassical) economic theoretical framework and methodology. The book is a monograph consisting of papers which have already or are about to be published, but they have been rewritten to fit together. The argument is developed through a traditional structure; the book starts out with a theoretical discussion before moving on to more empirical analysis. As such, the book moves on different levels of abstractions; first there is a conceptual discussion on knowledge and how this is diffused and used by firms. Following this is an empirical analysis on the knowledge base of a region, how different knowledge intensive firms are localized and what can explain this pattern, based on several impressive datasets from the Netherlands
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".