Climate Mitigation from a Renter-Centered Perspective: A Case Study of Boulder's SmartRegs Program
Bibliographic record
Abstract
My thesis is a renter-centered analysis of the City of Boulders SmartRegs rental energy efficiency standards, as a policymaking process that purports to address both climate and housing issues simultaneously, rather than one at the expense of the other. This focus on renters starts from the premise that housing and climate justice should be about the people most impacted. My preliminary findings indicate that, despite nearly all rental units meeting the SmartRegs basic energy efficiency requirements, SmartRegs did not result in improved tenant comfort and lower utility bills, as promised by the City of Boulder. I argue that SmartRegs was predominantly a carbon-focused policy to reduce emissions in rental buildings, rather than a renter-centered policy that improved housing quality issues or affordability for renters. I recommend that cities like Toronto learn from Boulder and proactively include renters in policymaking processes and protect them against unaffordable housing and local climate impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".