Addressing quality issues of historical GIS data: an example of Republican Beijing
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
This article addresses several issues related to historical GIS data using a project studying the social culture of Republican Beijing as an illustration. For large-scale historical GIS projects, certain data layers or themes are fundamental to and provide the context for various types of investigation. We suggested that these data may be regarded as framework data, similar to the concept of the core dataset identified in the US National Spatial Data Infrastructure (NSDI) framework, but in a GIS project context. Due to various reasons, most historical GIS data always invite concerns about their quality. We discussed how typical spatial data quality concepts are partially applicable to historical GIS data. We also highlighted the data quality aspects that are more significant to historical than contemporary GIS data. Compiling high-quality historical GIS data is challenging. We used the data layer of temple locations as an example to illustrate the process of using a set of principles to resolve the inconsistencies of data from multiple sources to deal with location accuracy and data completeness problems. Two common but related quality concerns of historical GIS data are their relatively low spatial resolution and imprecise locations. The original population dataset of Republican Beijing suffers from these two issues. Using ancillary data, more precise population locations and population distribution at a higher resolution were estimated. Compilation of historical GIS data requires fusing data of different sources in order to enhance the quality of the data.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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