Quarter Boundary in Historical Fabrics of Iranian Cities Case Study: Ali Gholi Agha Quarter, Isfahan
Bibliographic record
Abstract
The boundary of quarter is among the subjects that have been widely addressed in urban planning and urban design research.Clearly, several studies have examined the issue of "quarter boundaries in the historical fabric of Iranian cities", but the literature review shows that there are still ambiguities in this regard.There are two contradictory answers to this issue: one group of studies has set clear boundaries for quarters in the historical fabric of Iranian cities, while the other ones consider the boundaries of quarters in these fabrics to be vague and believe that each quarter will reach another quarter gradually.The purpose of this article is identifying the boundary of quarter in these fabrics.In this regard, Ali Gholi Agha quarter in the historical fabric of Isfahan was chosen as a case study and the qualitative research method of conventional content analysis was used.The boundary of Ali Gholi Agha quarter was studied in three ways of historical boundary, quarter boundary based on door-to-door interviews, and subjective boundary drawn by the inhabitants.Among these three ways, the historical boundary discovered based on the historical structure of access and quarter elements, shows the hidden order in the structure and provides a new method to discover the objective boundary of the quarter in the historical fabrics of Iranian cities.Interestingly, the accuracy of the achievements of this method is confirmed by two other boundaries.Finally, it is concluded that there is an objective boundary in some parts of the quarter.This boundary is often in dead ends and darbands (passes through the entrance of the houses in historic fabrics).Also, in the other parts of the quarter where there is no clear boundary, the boundary can be determined based on this objective boundary.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".