Improving Urban Quality Through Land Titling? Considerations from the Bamiyan Case
Bibliographic record
Abstract
Abstract The article discusses the Afghan land titling policies based on the case of Bamiyan Valley. It first presents the terms and conditions of the land titling policy in the Islamic Republic of Afghanistan since 2017, and then it illustrates its impact on the informal settlement of Zargaran (Bamiyan) based on the results of two surveys conducted in 2017 and 2021. As is the case elsewhere around the world, the assignment of formal property titles is generally welcomed by the majority of the population. Moreover, doing so has proven to encourage investments in the improvement of private establishments, and even in facilities for community purposes, thanks to the remarkable social bond that exists between the settlers. However, the denial of the entitlement in the parcels of Zargaran located inside and next to the UNESCO buffer zone has prevented the titling policy from reaching its full potential in terms of improvement of the social fabric and urban quality. Moreover, the increase in real estate values observed in the area calls for social policy measures to accompany the titling policy, so as to avoid the eviction of poorer segments of the population.
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 | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".