Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing "page protection" that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia's fundamental principles—that it is open to all contributors—and may hinder further improvement of the encyclopedia. In this paper, we examine the effect of page protection on article quality to better understand whether and when page protections are warranted. Using decade-long data on page protections from the English Wikipedia, we conduct a quasi-experimental study analyzing pages that received "requests for page protection"—written appeals submitted by Wikipedia editors to administrators to impose page protections. We match pages that indeed received page protection with similar pages that did not and quantify the causal effect of the interventions on a well-established measure of article quality. Our findings indicate that the effect of page protection on article quality depends on the characteristics of the page prior to the intervention: high-quality articles are affected positively, as opposed to low-quality articles that are impacted negatively. Subsequent analysis suggests that high-quality articles degrade when left unprotected, whereas low-quality articles improve. Overall, with our study, we outline page protections on Wikipedia and inform best practices on whether and when to protect an article.

Narrative-driven recommenders aim to provide personalized suggestions for user requests expressed in free-form text such as ''I want to watch a thriller with a mind-bending story, like Shutter Island.'' Although large language models (LLMs) have been shown to excel in processing general natural language queries, their effectiveness for handling such recommendation requests remains relatively unexplored. To close this gap, we compare the performance of 38 open- and closed-source LLMs of various sizes, such as LLama 3.2 and GPT-4o, in a movie recommendation setting. For this, we utilize a gold-standard, crowdworker-annotated dataset of posts from reddit's movie suggestion community and employ various prompting strategies, including zero-shot, identity, and few-shot prompting. Our findings demonstrate the ability of LLMs to generate contextually relevant movie recommendations, significantly outperforming other state-of-the-art approaches, such as doc2vec. While we find that closed-source and large-parameterized models generally perform best, medium-sized open-source models remain competitive, being only slightly outperformed by their more computationally expensive counterparts. Furthermore, we observe no significant differences across prompting strategies for most models, underscoring the effectiveness of simple approaches such as zero-shot prompting for narrative-driven recommendations. Overall, this work offers valuable insights for recommender system researchers as well as practitioners aiming to integrate LLMs into real-world recommendation tools.