When the High Road Becomes the Low Road: The Limits of High‐Technology Competition in<scp>F</scp>inland
Bibliographic record
Abstract
Abstract Globalization has generated increasing interest in technology‐intensive industries as a way to sustain national economic competitiveness. High‐technology growth is often conceptualized as a “high road” to prosperity, more amenable to private–public, industry–labor, and interfirm cooperation than tax, regulatory, or cost competitive strategies. While specialization in technology‐intensive industries does deliver several benefits, this article usesFinland's successful transformation into a high‐technology economy to highlight the significant economic and political risks associated with this strategy. Economically, movement into electronics exposedFinland to cost competition and disruptive technological innovations. Politically, high‐technology competition weakened the solidaristic ties that characterized postwar capitalism and the coordinating capacities that underpinned economic growth. In short, high‐technology growth exacerbated the problems it was supposed to solve. The article concludes by generalizing the argument to several non‐Nordic states.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".