Asset Recycling for Social Infrastructure in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Asset recycling (AR) has gained attention in the United States as a way of improving life cycle asset maintenance and realizing maximum value from existing public infrastructure. In an AR program, proceeds from leases or sales of mature, underutilized public assets are reinvested in much-needed infrastructure improvements. Although the benefits of AR are often noted in both academic and policy circles, the academic literature on AR has not yet explored AR’s application to social infrastructure. To address this gap, we explore the concept of AR and its relevance for U.S. social infrastructure. We first examine the steps and conceptual features of a “fix-it-first” AR approach to social infrastructure. We then use Infrastructure Ontario’s Capital Planning Program as a case study to highlight the potential viability of such programs. Finally, we conclude by discussing the benefits and challenges of adopting AR policies in the United States.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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 it