Why has Viimsi become the wealthiest community in post‐socialist Estonia?
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a qualitative study of economic development attempts to uncover the critical factors behind the recent economic rise of Viimsi, Estonia, to that of the wealthiest community in the country. Design/methodology/approach The roles of and interactions among various community stakeholder groups are explored through two sets of resident surveys. From these surveys, and a rich set of secondary material, a narrative emerges as to the factors behind Viimsi's success since 1991, when Estonia regained its independence from the former Soviet Union and its system of economic central planning. Findings It was found that among the key components explaining why Viimsi has become the wealthiest community in Estonia are its extraordinary openness in embracing the market system, combined with a consensus‐based plan among community stakeholders on balanced development, a dependable government, and a level of solidarity among fellow residents in facing change. Originality/value This paper describes one small community's experience in the global world, and offers observations on successful strategies for a community navigating in it.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".