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Record W2080101761 · doi:10.1068/b3048

Analyzing Planning and Design Discourses

2004· article· en· W2080101761 on OpenAlex
Sandeep Kumar, Varkki Pallathucheril

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

VenueEnvironment and Planning B Planning and Design · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRepresentation (politics)Argument (complex analysis)Discourse analysisSociologyEpistemologyTable (database)Power (physics)Order (exchange)Critical discourse analysisComputer scienceLinguisticsPolitical sciencePoliticsData miningPhilosophyLaw

Abstract

fetched live from OpenAlex

The term ‘discourse’ is used to describe the entangled and contested transactions through which real-world planning and policy issues are addressed. Studies of discourses have become an important way of understanding how power is mediated in planning, but the methods through which discourses are identified and evaluated is as yet unclear in the literature. Here, we describe our attempt—with still only limited success—to map discourses using a method that extends the work of Toulmin and Gasper and George. Our method consists of a tabular representation of argument structure to depict the content and structure of a discourse, and a graphical index to the discourse table to reveal higher order patterns in the discourse. Using discourse pertaining to a real-life design-review case, we demonstrate how our approach allows us to understand the internal structure of that discourse better. We conclude with suggestions for how the method might be further improved.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.279
Teacher spread0.231 · 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