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We live in an age where legislation and regulation make an increasing impact on our lives. The expansion of legislative and regulatory activities and outputs in the last century and especially in the last four decades has been followed by a growth of scholarly interest. However, far too little attention has been paid to the conceptual and empirical exploration of the relationships between legislation and regulation, and especially to the democratic consequences of their co-evolution, substitutivity, and intersection. This essay provides an introduction to the special issue on legislation and regulation. After introducing the topic, it presents three analytical distinctions between legislation and regulation and introduces the contributions to the issue.

This paper addresses the task of automatically detecting narrative structures in raw texts. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to news articles, motivated by their growing social impact as well as their role in creating and shaping public opinion.We introduce CompRes -the first dataset for narrative structure in news media. We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1,099 sentences) collected from news and partisan websites. We use the annotated dataset to train several supervised models to identify the different narrative elements, achieving an F 1 score of up to 0.7. We conclude by suggesting several promising directions for future work.

Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories.We introduce NEAT (Narrative Elements An-noTation) -a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains 1 . We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F 1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.

The COVID-19 pandemic brought new challenges in all aspects of life. It largely brought the sports sector to a halt: major events were postponed or canceled, while gyms and training centers were closed due to repeated lockdowns and social distancing rules and regulations. In the private sports sector, some instructors adopted technological means of maintaining contact with their students in an attempt to retain customers and maintain a high volume of cash flow. Our work focuses on the martial arts (MA) sector in Israel during two crucial periods in 2020: The first lockdown of March through June, when all sports activities were banned, and the period following it, when trainers were allowed to commence training under some regulations. Using data collected from 199 MA instructors, we test for their level and means of engagement with trainees during the lockdown, and the impact these had on customer retention in the period that followed. Using latent class analysis, we establish an empirically based typology of retention schemes (low contact, high contact, and maverick), and test whether these influenced the financial performance of MA studios. Our findings show that the financial damage and the return rate of trainees do not vary between the three types. We offer some insights into the uniqueness of the MA field, and how this may explain these counter-intuitive results.