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Record W7112006862

The Unspoken Narratives of the Empty Quarter

2021· article· en· W7112006862 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsDesert (philosophy)Quarter (Canadian coin)PeninsulaNarrativePoliticsNatural resourceNatural (archaeology)Resource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

The desert, commonly understood as a barren and infertile landscape, is not empty. This thesis reads the desert landscape as an archive full of social, economic, and political narratives using the Empty Quarter as a case study. The Empty Quarter, in Arabic Rub al-Khali, stretches across the southern part of the Arabian Peninsula including Oman, Saudi Arabia, the United Arab Emirates, and Yemen. The overarching understanding of deserts as a void has obscured the Empty Quarter’s image as a home to Bedouin tribes and a site of natural resource extraction, agricultural land, and a testing ground for scientific research. Furthermore, the inability to understand the desert ecology is causing current urban processes to be resource intensive as the adjacent cities expand. By providing a new reading of the desert ecology, this thesis speculates how design and planning in arid regions can mediate between social values, aesthetics, and environmental challenges to arrive at ecological urban development. This thesis draws from multiple sources, including the analysis of archival materials, historic maps, artworks, field observation, myths, and tribal poetry, in order to arrive at a novel understanding of the Empty Quarter’s ecosystem. It is not a void, but rather a space that adjacent cities depend upon to meet the social and material needs of their inhabitants.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.268
Teacher spread0.239 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDigital Access to Scholarship at Harvard (DASH) (Harvard University)Same topicMiddle East and Rwanda ConflictsFrench-language works237,207