Evaluating Just Transit-Oriented Development Planning in the Community of Jefferson Park, Chicago
Bibliographic record
Abstract
Transit-Oriented Development (TOD) is a specialized planning framework used for coordinating land-use around transit stations. A common approach with TOD is to promote a heterogenous mix of land-uses along with design upgrades to support smart growth and increased transit access (Cervero, 2004). This does not mean that public officials should simply change density guidelines to increase ridership. To achieve optimal success, scholars have determined that planners should take the opportunity to create a community-lead and focused comprehensive plan. Defining a community’s relationship to local infrastructure requires planners to be mindful of whether the bureaucratic process they follow is actually achieving or missing the true ‘collective vision’ (Forester, 1982; Baum, 1983). Therefore, the purpose of this Major Research Project is evaluating current literature as well as the progress of an ongoing TOD plan to create a concise strategy for just TOD planning practice. Moreover, this paper will provide suggestions for actions that the community could follow to support civic engagement. This paper looks at the current progress of the Jefferson Park Community Master Plan (2018), a TOD plan for the respective community on the northwest side of Chicago, Illinois. This site was chosen due to its history as a commuter suburb and its prime location as a transit hub on the outskirts of the city. The respective hub services all of the region’s transit agencies, is divided by high-volume freeway, and has a strong community network involved in local affairs. First, I consult existing literature and articles to determine the best practices for enabling Just TOD in this community. Second, I conduct a study examining whether the plan’s proposed actions are being implemented in reality. I then propose a new set of values, action items and a community hub that further achieves the community-oriented vision that the plan focuses on.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".