Digging for the truth: A combined method to analyze the literature on stream daylighting
Bibliographic record
Abstract
To date, review articles examining stream daylighting (de-culverting buried streams) are limited in scope, report on only a fraction of available publications, and focus on publications’ explicit (manifest) content rather than their underlying constructs (latent content). This review combines the methods of systematic literature reviews and inductive content analysis to better understand the scope and nature of the literature on stream daylighting. The study investigates four themes: the disciplines, terminologies, definitions, and case studies and their interconnections and covers all relevant English-language sources since 1992 through 2018. The results reveal three findings with implications for future research: 1) there is a dearth in studies that tackle crucial contemporary challenges like climate change and studies that delve into the complex connections among the socio-cultural, physical planning, environmental, and economic dimensions of stream daylighting, such as socio-environmental justice, architecture, and urban design; 2) the terminology is inconsistent and a clear definition is absent; 3) Some important stream daylighting cases are overlooked, such as Zürich’s (Switzerland) city-wide initiative and Riyadh’s (Saudi Arabia) first arid climate initiative. The inclusion of such case studies in the literature impacts the perception of stream daylighting and expands the scope and dimensions of this practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".