Urban Shrinkage in Liepāja : Awareness of population decline in the planning process
Bibliographic record
Abstract
The aim of the study is to investigate the current state of awareness of urban shrinkage inLiepājaby the key actors involved in the planning process. Last couple of hundred years have brought many transformations in urbanity that was always accompanied by the growth of the population and expansion of the city. However, the new patterns of urban development emerged in the last decades all over the globe, causing cities to lose the inhabitants resulting in urban shrinkage.Liepāja, the third largest city inLatvia, has lost a quarter of its population in last two decades and the trend continues. The long-term municipal planning document is being presented during this research in a light of which the research question is asked: “What is the current state of awareness of urban shrinkage inLiepājaby the key actors?” Utilising Flyvbjerg’s phronetic form of inquiry in combination with case study and repeated semi-structured interviews, the dominant planning views related to urban shrinkage are sought and analysed. The research identifies three underlying causalities that shape the decisions in planning and leave formidable consequences for the future of the city. The causalities identified and discussed in this paper are (1) the planning legacy; (2) the misconception; and (3) the political sensitivity of the urban shrinkage.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".