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Wikipedia's low barriers to participation have the unintended effect of attracting a large number of articles whose topics do not meet Wikipedia's inclusion standards. Many are quickly deleted, often causing their creators to stop contributing to the site. We collect and make available several datasets of deleted articles, heretofore inaccessible, and use them to create a model that can predict with high precision whether or not an article will be deleted. We report precision of 98.6% and recall of 97.5% in the best case and high precision with lower, but still useful, recall, in the most difficult case. We propose to deploy a system utilizing this model on Wikipedia as a set of decision-support tools to help article creators evaluate and improve their articles before posting, and new article patrollers make more informed decisions about which articles to delete and which to improve.
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Stored only when both papers are in the corpus — 6 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.