The Expansion of the Finance Industry and Its Impact on the Economy: A Territorial Approach Based on Swiss Pension Funds
Bibliographic record
Abstract
abstract A new economic geography of finance is emerging, and the current “financialization” of contemporary economies has contributed greatly to the reshaping of the economic landscape. How can these changes be understood and interpreted, especially from a territorial point of view? There are two contradictory economic theories regarding the tangible effects of the rise of the finance industry. According to neoclassical financial theorists, the finance industry's success is based on its positive effects on the real economy through its capacity to allocate financial resources efficiently. An alternative approach, adopted here, posits that finance does not merely mirror the real economy and that the financial economy, far from being a simple instrument for the allocation of capital, has its own autonomy, its own logic of development and expansion. A series of complex, and sometimes contradictory, connections link financial markets and the real economy, and there are some tensions between them, calling into question the coherence of the regional and national economies that follow from them. Moreover, the territorial approach shows how the mobility/liquidity of capital and the changing dimensions of new regions and countries are central to the finance industry's functioning. This article builds an understanding of the financial system through the lens of pension funds and highlights the impact of such a system on the real economy and its geography.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".