MétaCan
Menu
Back to cohort
Record W7128511691 · doi:10.64903/1480-6800.18.4.299

The Impact of Rapid Motorization and Urban Growth: An Analysis of the City of Doha, Qatar

2015· article· W7128511691 on OpenAlexvenueno aff

Bibliographic record

VenueArab world geographer · 2015
Typearticle
Language
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportUrban planningCapital cityCapital (architecture)Transportation planningPrivate capitalPublic policy

Abstract

fetched live from OpenAlex

Since the 1990s, Doha, the capital city of Qatar, has experienced rapid urban growth along with rapid motorization, which together have created new opportunities but also important challenges for the Qatari government. Urban sprawl, lack of planning strategy, harsh weather, and an absence of public transport are some of the reasons that private vehicles dominate the city. This article discusses three of the main impacts of rapid growth and motorization—environmental problems, physical fragmentation of the city, and social impact—and analyzes the Qatari government’s current efforts to resolve these problems. The article concludes that local policies should focus on two main areas of intervention: first, integrating land-use and transport planning; and, second, supporting a multi-modal approach to transportation (integrating different modes) that facilitates the shift from one option to another. Findings can be applied by extension to other major Gulf cities.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.022
GPT teacher head0.290
Teacher spread0.267 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueArab world geographerSame topicSocioeconomic Development in MENAFrench-language works237,207