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
If urban development plans were just target patterns to be achieved, conventional data structures in Geographic information systems (GIS) would be sufficient. Urban development plans have a strong spatial component, but recent literature in planning emphasizes that plans are about actions and relationships among them. These relationships include interdependence, substitutability, priority, and parthood. In order to support planning, GIScience should devise data structures and queries to support reasoning with these relationships. This article shows how relationships encoded within each of a set of plans, using a recently developed data model, can be used to infer the relationships of actions among these plans. Simple databases and use cases based on real situations in McHenry County, Illinois are used to demonstrate that these relationships can be encoded and queried. The results demonstrate that previously discovered semantic relationships can be used to discover additional relationships across plans, thereby enriching the decision making. The approach provides a systematic way of structuring the information in plans to support making and using plans.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 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