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Record W3009728175 · doi:10.17645/up.v5i1.2857

Rethinking Planning Systems: A Plea for Self-Assessment and Comparative Learning

2020· article· en· W3009728175 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

VenueUrban Planning · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPleaVariety (cybernetics)GrassrootsContextualizationContext (archaeology)SociologyPolitical scienceManagement scienceEngineering ethicsKnowledge managementComputer scienceEngineeringGeographyArtificial intelligenceInterpretation (philosophy)

Abstract

fetched live from OpenAlex

<p class="Boxbodytext">The authors reflect on recent experiences at UN-Habitat and other international organizations to rethink the roles of planning towards larger development goals and to reform planning systems in places most in need of them. They consider the difficulties but ultimate necessity to learn from a variety of contexts and experiences to articulate general orientations for planning and planning reform which can partly transcend context. Within the variety of planning experiences, and the experiences of lack of planning, one can discern principles which can be applied in many contexts, yet those include principles of contextualization and learning. Comparative learning underpins the attempts at finding general principles, and the local application of those principles further triggers processes of learning, including comparative learning. Local and grassroots planning capacity building is vital to locally apply and contextualize international planning guidelines.</p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.455
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.079
GPT teacher head0.272
Teacher spread0.193 · 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