Transforming Precedent: A Systematic Review Methodology for Informed Planning Decisions
Bibliographic record
Abstract
Urban planners are often tasked with locating, assessing, and integrating multilevel policy, regulatory, administrative, and governance information. Through a recent systematic literature review (SLR) of Canadian municipal interventions targeting women’s equity, this paper answers the following research question: how can planners use an SLR methodology to increase the rigor and transparency of precedent research within practice? This article highlights the contributions and limitations of applying an SLR methodology to research within practice and demonstrates how planners can modify and apply this approach to standardize the collection and assessment of precedent to contribute to informed planning decisions.
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 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 | 0.494 | 0.622 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.055 | 0.043 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".