Attracting Investments and Companies: Federal Multi-Level Collaboration in Switzerland and Canada
Bibliographic record
Abstract
While tackling the issue of place attractiveness for companies and investments in Canada and Switzerland at large, this research focuses on federal multi-level collaboration with two case studies: Ontario and Western Switzerland. Based on empirical data gathered from semi-structured interviews ([Formula: see text]) and surveys ([Formula: see text]) as well as on secondary data, it provides an analysis of the perception of intergovernmental collaboration by economic developers and a mapping of the challenges identified in both the Canadian and the Swiss contexts. A comparative perspective, complemented by data regarding business decision-making ([Formula: see text]), allows us to draw lessons for economic developers in both countries, aiming at extending both academic and practitioners’ literatures. Findings show that the federal contexts in which attractive strategies occur cause specific challenges for economic developers. While judging that the system works well in general, the majority of Canadian economic developers interviewed mentioned different problems to solve, such as the confusion for companies generated by a federal multi-level system and the need for more business-oriented strategies away from political concerns. The majority of Swiss economic developers interviewed also acknowledged issues caused by the federal system and wished for improvements regarding coordination between federal entities and levels. In this regard, pragmatism is perceived as a crucial factor for the implementation of successful attractive strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".