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Record W4401806610 · doi:10.1007/978-3-031-54816-1_9

Improving Urban Quality Through Land Titling? Considerations from the Bamiyan Case

2024· book-chapter· en· W4401806610 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch for development · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsLand titlingQuality (philosophy)Environmental planningBusinessGeographyArchaeologyAgricultureLand tenurePhilosophyEpistemology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.372
GPT teacher head0.456
Teacher spread0.084 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it