Gangani Ariyarathne, Isuru Ariyarathne
We lifted 6 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| shapely/shapely | canonical | 0 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| deprecate_positional | Not yet run | shapely/shapely/src/shapely/decorators.py code served (permissive licence) · get_code("6f2bd1df6da08f3c") |
| interpret_origin | Not yet run | shapely/shapely/src/shapely/affinity.py code served (permissive licence) · get_code("f25a2618ab3db882") |
| multithreading_enabled | Not yet run | shapely/shapely/src/shapely/decorators.py code served (permissive licence) · get_code("d83194b2cc23dbb2") |
| register_vcs_handler | Not yet run | shapely/shapely/src/shapely/_version.py code served (permissive licence) · get_code("c55cafde08716f01") |
| run_command | Not yet run | shapely/shapely/src/shapely/_version.py code served (permissive licence) · get_code("dbb37355631dabcf") |
| versions_from_parentdir | Not yet run | shapely/shapely/src/shapely/_version.py code served (permissive licence) · get_code("5032e3b81c0fffb3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Local journalism is vital in democratic societies where it informs people about local issues like, school board elections, small businesses, local health services, etc. But mounting economic pressures have made it increasingly difficult for local news stations to report these issues, underscoring the need to identify the salient geographical locations covered in local news (geo-foci). In response, we propose a novel geo-foci model for labeling US local news articles with the geographic locations (i.e., the names of counties, cities, states, countries) central to their subject matter. First, we manually labeled US local news articles from all 50 states with four administrative division labels (local, state, national, and international) corresponding to their geo-foci, and none for articles without a geographic focus. Second, we extracted and disambiguated geographic locations from them using Large Language Models (LLMs), since local news often contains ambiguous geographic entities (e.g., Paris, Texas vs. Paris, France). LLMs outperformed all eight geographic entity disambiguation methods we evaluated. Third, we engineered a rich set of spatial-semantic features capturing the prominence, frequency, and contextual positions of geographic entities. Using these features, we trained a classifier to accurately (𝐹 1 : 0.86) detect the geographic foci of US local news articles. Our model could be applied to assess shifts from local to national narratives, and more broadly, enable researchers to better study local media.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.00787")
get_code_for_paper("2603.00787")
have("2603.00787")
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