Cross-evaluation of metrics to estimate the significance of creative works

"Give My Daughter the Shot!": A Content Analysis of the Depiction of Patients with Cancer Pain and Their Management in Hollywood Films

INTRODUCTION

Cinemeducation, the pedagogical use of films, has been used in a variety of clinical disciplines. To date, no studies have looked at the use of film depictions of cancer pain and its management in clinical education. We investigated how patients with cancer pain and their management are depicted in Hollywood films to determine whether there is content that would be amenable to use for cancer pain assessment and management education.

METHODS

A qualitative content analysis was performed. Films that contained characters with or references to cancer pain were searched for using the International Movie Database, the Literature Arts Medicine Database, the History of Medicine and Medical Humanities Database, and Medicine on Screen. After review, 4 films were identified for review and analysis.

RESULTS

Themes that emerged from the analysis concerned the films' depictions of characters with pain, their healthcare providers, the therapies used for pain management, and the setting in which pain management was provided.

CONCLUSIONS

This study demonstrates that patients with cancer pain are depicted in a compassionate manner. Pain management focused on the use of opioids. The settings in which patients received pain management was depicted as not being amenable to providing holistic care. This variety of topics related to pain management covered in the films make them amenable to use in cinemeducation. This study therefore forms the basis for future work developing film-based cancer education modules.

Popcorn in the pain clinic: A content analysis of the depiction of patients with chronic pain and their management in motion pictures

The watching of films is popular and accessible to broad segments of the population. The depiction of medical conditions in films has the potential to affect the public's perception of them and contribute to stereotypes and stigma. We investigated how patients with chronic pain and their management are depicted in feature films. Films that contained characters with or references to chronic pain were searched for using databases such as the International Movie Database. Themes that emerged from the content analysis revolved around the films' depictions of characters with pain, their health care providers, and therapies for pain management. Patients with chronic pain were depicted in various ways, including in manners that could elicit empathy from audiences or that might contribute to the development of negative stereotypes about them. The attitudes of health care professionals toward patients with chronic pain ranged from compassionate to dispassionate. Pain management was typically depicted as lacking in breadth or using multidisciplinary approaches with a focus on pharmacological management. The variety of topics related to chronic pain depicted in feature films lends to their use in medical education strategies to better inform health care professions trainees about chronic pain management.

Collaborations and Innovation in Partitioned Industries: An Analysis of U.S. Feature Film Coproductions

In partitioned industries, a small number of generalist organizations occupy the center of the market, whereas a much larger number of specialists populate the periphery. The role of collaborations within and across the center-periphery boundary in these industries has been underexplored. We propose that hybrid collaborations between organizations in the center and periphery—combining the broad resource base of generalists with the focused knowledge of specialists—encourage product innovation and result in enhanced organizational adaptation for both populations. We test these ideas in the U.S. motion picture industry, where film production companies face significant unpredictability of success and fluctuating audience tastes. We find that generalist and specialist production companies that partner to produce films introduce more creative content in their films compared with those that collaborate in the same population or produce alone. Generalist film companies benefit further from these collaborations through increased competitive differentiation of their films from other generalists in subsequent productions, whereas specialists experience lower exit rates. These findings suggest that interorganizational collaborations between generalists and specialists provide effective adaptive strategies to compete in markets with uncertain demand and shifting audience preferences. These strategies can sustain, rather than weaken, industry partitioning.

The shortlist effect: nestedness contributions as a tool to explain cultural success

Detecting the forces behind the success or failure of cultural products, such as books or films, remains a challenge. Three such forces are drift, context-biased selection and selection based on content - when things succeed because of their intrinsic appeal. We propose a tool to study content-biased selection in sets of cultural collections - e.g. libraries or movie collections - based on the 'shortlist effect': the fact that smaller collections are more selective and more likely to favour highly appealing items over others. We use a model to show that, when the shortlist effect is at work, content-biased cultural selection is associated with greater nestedness in sets of collections. Having established empirically the existence of the shortlist effect, and of content-biased selection, in 28 sets of movie collections, we show that nestedness contributions can be used to estimate to what extent specific movies owe their success to their intrinsic properties. This method can be used in a wide range of datasets to detect the items that owe their success to their intrinsic appeal, as opposed to 'hidden gems' or 'accidental hits'.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

Lights, camera, scalpel: a lookback at 100 years of plastic surgery on the silver screen

BACKGROUND

The presentation of medical topics in the cinema can greatly influence the public's understanding and perception of a medical field, with regard to the doctors and surgeons, medical diagnosis, and treatment and outcome expectations. This study aims to evaluate the representation of plastic surgery in commercial films that include a character with a link to plastic surgery, either as a patient or surgeon.

METHODS

The international film databases Internet Movie Database (IMDb), The American Film Institute (AFI), and British Film Institute (BFI) were searched from 1919 to 2019 to identify feature-length films with a link to plastic surgery. Movies were visualized and analyzed to identify themes, and the portrayal of plastic surgery was rated negative or positive, and realistic or unrealistic.

RESULTS

A total of 223 films were identified from 1919 to 2019, produced across 19 countries. Various genres were identified including drama (41), comedy (25), and crime (23). A total of 172 patient characters and 57 surgeon characters were identified as major roles, and a further 102 surgeons as minor roles. Disparities were noted in presentation of surgeons, both in terms of race and gender, with the vast majority of surgeons being white and male. In total only 11 female surgeons were portrayed and only one black surgeon. Thirteen themes emerged: face transplantation, crime, future society, surgeon mental status, body dysmorphic disorder, vanity, anti-aging, race, reconstructive surgery, deformity, scarring, burns, and gender transitioning. The majority of films (146/223) provide an unrealistic view of plastic surgery, painted under a negative light (80/146). Only 20 films provide a positive realistic image (24/77).

CONCLUSIONS

There exists a complicated relationship between plastic surgery and its representation on film. Surgical and aesthetic interventions are portrayed unrealistically, with surgeons and patients presented negatively, perpetuating stigma, particularly with regard to cosmetic surgery. Cinema is also characterized by lack of representation of female and non-white surgeons. Recruitment of surgeons as technical advisors would help present a more realistic, representative view, without necessarily sacrificing creativity.Level of evidence: Not ratable.

Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks

Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time-aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks.

Portrayal of Immunization in American Cinema: 1925 to 2016

The American media often disseminates antivaccination messages. Cinema in particular reaches many individuals and influences attitudes regarding high-risk behaviors such as smoking and alcohol use. We hypothesized that negative cinematic portrayals of immunization have increased over the last 3 decades. Films released in the United States featuring immunization through 2016 were identified on IMDb and viewed in their entirety by 2 reviewers. Themes were recorded, and the portrayal of immunization (positive, negative, or mixed) across each decade was assessed in a logistic regression model. Cultural references attributed to films (eg, television references) were recorded from the "connection" feature on IMDb. Fifty relevant films were identified (1925-2016). Negative/mixed portrayals of immunization were more frequent after 1990 (odds ratio = 4.0, 95% confidence interval = 1.2-13.5), and films with positive immunization portrayals garnered significantly fewer cultural references than films with negative/mixed portrayals (mean = 9.2 vs 56.2, P = .048). American cinema features increasingly negative portrayals of immunization.

Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria

The analysis of citations to scientific publications has become a tool that is used in the evaluation of a researcher's work; especially in the face of an ever-increasing production volume^1-6. Despite the acknowledged shortcomings of citation analysis and the ongoing debate on the meaning of citations^7,8, citations are still primarily viewed as endorsements and as indicators of the influence of the cited reference, regardless of the context of the citation. However, only recently has attention^9,10 been given to the connection between contextual information and the success of citing and cited papers, primarily because of the lack of extensive databases that cover both types of metadata. Here we address this issue by studying the usage of citations throughout the full text of 156,558 articles published by the Public Library of Science (PLoS), and by tracing their bibliometric history from among 60 million records obtained from the Web of Science. We find universal patterns of variation in the usage of citations across paper sections¹¹. Notably, we find differences in microlevel citation patterns that were dependent on the ultimate impact of the citing paper itself; publications from high-impact groups tend to cite younger references, as well as more very young and better-cited references. Our study provides a quantitative approach to addressing the long-standing issue that not all citations count the same.

Identification of key films and personalities in the history of cinema from a Western perspective

The success of a film is usually measured through its box-office revenue or through the opinion of professional critics; such measures, however, may be influenced by external factors, such as advertisement or trends, and are not able to capture the impact of a film over time. Thanks to the recent availability of data on references among movies, some researchers have started to use citations patterns as an alternative method for ranking movies. In this paper, we propose a novel ranking method for films based on the network of references among movies, calculated by combining four well known centrality indexes: in-degree, closeness, harmonic and PageRank. Our objective is to measure the success of a movie by accounting how much it has influenced other movies produced after its release, from both the artistic and the economic point of view. We apply our method on a subset of the IMDb (Internet Movie Database) citation network consisting of around 47,000 international movies, and we derive a list of films that can be considered milestones in the history of cinema. For each movie we also collect data on its year of release, genres and countries of production, to analyze trends and patterns in the film industry according to such features. We also collect data on 20,000 directors and almost 400,000 performers (actors and actresses), and we use the network of references and our score of movies for evaluating their career, and for ranking them. Since the IMDb dataset we employ is highly biased toward European and North American movies and personalities, our findings can be considered relevant principally for Western culture.

Hot streaks in artistic, cultural, and scientific careers

The hot streak-loosely defined as 'winning begets more winnings'-highlights a specific period during which an individual's performance is substantially better than his or her typical performance. Although hot streaks have been widely debated in sports^1,2, gambling^3-5 and financial markets^6,7 over the past several decades, little is known about whether they apply to individual careers. Here, building on rich literature on the lifecycle of creativity^8-22, we collected large-scale career histories of individual artists, film directors and scientists, tracing the artworks, films and scientific publications they produced. We find that, across all three domains, hit works within a career show a high degree of temporal regularity, with each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that these observations can be explained by a simple hot-streak model, allowing us to probe quantitatively the hot streak phenomenon governing individual careers. We find this phenomemon to be remarkably universal across diverse domains: hot streaks are ubiquitous yet usually unique across different careers. The hot streak emerges randomly within an individual's sequence of works, is temporally localized, and is not associated with any detectable change in productivity. We show that, because works produced during hot streaks garner substantially more impact, the uncovered hot streaks fundamentally drive the collective impact of an individual, and ignoring this leads us to systematically overestimate or underestimate the future impact of a career. These results not only deepen our quantitative understanding of patterns that govern individual ingenuity and success, but also may have implications for identifying and nurturing individuals whose work will have lasting impact.

Randomizing growing networks with a time-respecting null model

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology-a time-respecting null model-that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Significance and popularity in music production

Creative industries constantly strive for fame and popularity. Though highly desirable, popularity is not the only achievement artistic creations might ever acquire. Leaving a longstanding mark in the global production and influencing future works is an even more important achievement, usually acknowledged by experts and scholars. 'Significant' or 'influential' works are not always well known to the public or have sometimes been long forgotten by the vast majority. In this paper, we focus on the duality between what is successful and what is significant in the musical context. To this end, we consider a user-generated set of tags collected through an online music platform, whose evolving co-occurrence network mirrors the growing conceptual space underlying music production. We define a set of general metrics aiming at characterizing music albums throughout history, and their relationships with the overall musical production. We show how these metrics allow to classify albums according to their current popularity or their belonging to expert-made lists of important albums. In this way, we provide the scientific community and the public at large with quantitative tools to tell apart popular albums from culturally or aesthetically relevant artworks. The generality of the methodology presented here lends itself to be used in all those fields where innovation and creativity are in play.

Multiple Citation Indicators and Their Composite across Scientific Disciplines

Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011–2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.

Citation indicators addressing total impact, co-authorship, and author positions offer complementary insights about impact. This article shows that a composite score including six citation indicators identifies extremely influential scientists better than single indicators.

Multiple citation indicators are used in science and scientific evaluation. With an increasing proportion of papers co-authored by many researchers, it is important to account for the relative contributions of different co-authors. We explored multiple citation indicators that address total impact, co-authorship adjustment, and author order (in particular, single, first, or last position authorships, since these positions suggest pivotal contributions to the work). We evaluated the top 30,000 scientists in 2013 based on each of six citation indicators (84,116 total scientists assessed) and also developed a composite score that combines the six indicators. Different scientists populated the top ranks when different indicators were used. Many Nobel laureates and other influential scientists rank among the top 1,000 with the composite indicator, but rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations had no single/first/last-authored cited paper. More Nobel laureates are among the top authors when authors are ranked by the composite score than by single indicators. Multiple indicators and their composite give a more comprehensive picture of impact, although no method can pick all the best scientists.