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Record W2183906005 · doi:10.24043/isj.306

The epistemology of a sea view: mindscapes of space, power and value in Mumbai

2014· article· en· W2183906005 on OpenAlexaffvenue
Ramanathan Swaminathan

Bibliographic record

VenueIsland Studies Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsArchipelagic stateTerritorialitySpace (punctuation)Value (mathematics)SociologyPower (physics)SeascapeCONTESTPoint (geometry)EpistemologyGeographyAestheticsLawPolitical scienceComputer sciencePhilosophyLinguisticsMathematics

Abstract

fetched live from OpenAlex

Mumbai is a collection of seven islands strung together by a historically layered process of reclamation, migration and resettlement. The built landscape reflects the unique geographical characteristics of Mumbai’s archipelagic nature. This paper first explores the material, non-material and epistemological contours of space in Mumbai. It establishes that the physical contouring of space through institutional, administrative and non-institutional mechanisms are architected by complex notions of distance from the city’s coasts. Second, the paper unravels the unique discursive strands of space, spatiality and territoriality of Mumbai. It builds the case that the city’s collective imaginary of value is foundationally linked to the archipelagic nature of the city. Third, the paper deconstructs the complex power dynamics how a sea view turns into a gaze: one that is at once a point of view as it is a factor that provides physical and mental form to space. In conclusion, the paper makes the case that the mindscapes of space, value and power in Mumbai have archipelagic material foundations.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.056
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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.

Study designQualitative
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

Citations17
Published2014
Admission routes2
Has abstractyes

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