Transition stories : politics of urban living space in Tirana city region, Albania
Bibliographic record
Abstract
This thesis contributes to the geography of urban transition and to the expansion of knowledge of the post socialist city. Although many cities in transitional countries have inherited similar forms of housing and infrastructure provision and urban development from communist regime, there are important differences in the ways in which the recent decentralization and deregulation of urban development has impacted on, and activated, suburban land development interests and processes. There are different 'transition stories' yet to be revealed about urban development patterns, processes and politics in particular countries. A case in point is the process of suburban development in the capital city-region of Albania, Tirana, which exhibits some unique regulatory conflicts as it moves towards an ostensibly more liberal, free-market and decentralized urban development system.By focusing on the case of Tirana, this thesis provides an example of such multiple 'transition stories' of post-socialist urban development and its politics. This thesis aims to contribute to the limited literature on the politics of urban development in Albania during the transition period. It forms the context for the empirical analysis of local transition stories in Tirana city region, examined from the perspective of property rights and livelihood strategies. It concerns the underpinning role of property knowledge in shaping livelihood strategies in the post socialist city.One key argument that transcends all research questions is that local politics in the living urban space of the Tirana city region is not yet competitive (of a city and suburban area), but remains fundamentally contested. An explanatory analysis is presented of contemporary policy based on secondary and primary data about not only the regulation of new urban spaces, especially in suburban areas of Tirana, but also local property knowledge as it has evolved and is shared between different actors in Tirana city region. This will reveal how transition stories intersect and interact with the new socio-political context. This thesis was written between 2006 and 2010 and used a triangulation of qualitative approach through semi structure interviews, focus groups, and direct observation of local officials in Tirana city region.
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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 | 0.012 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".