Systematic content analysis: A combined method to analyze the literature on the daylighting (de-culverting) of urban streams
Bibliographic record
Abstract
In this era of climate change, novel nature-based solutions, like the daylighting (de-culverting) of streams, that enhance the socio-ecological resilience are gaining prominence. Yet, the growing body of literature on stream daylighting spreads over an array of seemingly disconnected disciplines and lacks consistency in the terminology and the definitions of the practice. Moreover, nearly all the literature review studies on stream daylighting (mostly produced since 2000) underscore, as their point of departure, the daylighting projects rather than a review of the literature's content per se. Therefore, this study reassesses the literature on stream daylighting with a particular focus on its role, as a nature-based solution, for climate change mitigation and adaptation and for socio-environmental justice. We combine the systematic literature review (an all-encompassing review of the available literature on stream daylighting) with the inductive content analysis (an in-depth analysis of this literature's nature). Accordingly, we investigate all the relevant English-language publications since the first peer reviewed article on stream daylighting was published in 1992 until the end of 2018 to analyze four themes: the disciplines and sub-disciplines of the literature; the terminologies and synonyms of stream daylighting; the definitions of stream daylighting; and the case studies tackled in the literature.•We develop a method that combines a systematic review of the stream daylighting literature and inductive content analysis.•The method provides insights on the stream daylighting's literature's disciplines, terminologies, synonyms and case studies.•The method is adaptable particularly, to nascent areas of study where sources' numbers range between 100-200.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.196 | 0.284 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.059 | 0.043 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".