MétaCan
Menu
Back to cohort
Record W4412887575 · doi:10.1080/09640568.2025.2526614

Urban stream daylighting as a nature-based solution: transformative or incremental? A scaffolded review of Seoul’s Cheonggyecheon and Zürich’s <i>Bächkonzept</i>

2025· review· en· W4412887575 on OpenAlexafffund
Luna Khirfan

Bibliographic record

VenueJournal of Environmental Planning and Management · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaFederation for the Humanities and Social Sciences
KeywordsTransformative learningDaylightingArchitectural engineeringWalkabilityEnvironmental planningGeographySociologyEngineeringCivil engineeringBuilt environmentPedagogy

Abstract

fetched live from OpenAlex

As the climate crisis drives the uptake of nature-based solutions (NbS), daylighting culverted urban streams is gaining traction. Yet, questions abound on how to approach stream daylighting and on its impacts. This study responds through a scaffolded literature review: 1) a scoping review on transformative and incremental adaptations that led to a comparative framework of four transformative-incremental adaptations criteria and their corresponding 18 parameters; and 2) a systematic content analysis of two fundamentally different approaches to stream daylighting: Seoul’s (S. Korea) mega-project that daylighted over 6 kilometers of the Cheonggyecheon and Zürich’s (Switzerland) small-scale Bächkonzept that daylighted over 25 kilometers of small stream segments. The findings reveal that, notwithstanding temporal, scale, and cost differences, Seoul’s Cheonggyecheon and Zürich’s Bächkonzept are both transformational, albeit differently. Yet, neither fits the abrupt (maintained functions, changed structures) nor the directed (changed functions, maintained structures) transformations; instead, lessons are gleaned from these two daylighting interventions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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
GenreReview

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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Environmental Planning and ManagementSame topicUrban Green Space and HealthFrench-language works237,207