Conversations with Scholars of American Popular Culture: Philippa Gates
Notice bibliographique
Résumé
Philippa Gates is an Associate Professor of Film Studies at Wilfrid Laurier University in Canada as well as Film Studies Program Coordinator. She holds a Ph.D. in Film and Visual Culture from Exeter in the UK. Her publications include Detecting Men: Masculinity and the Hollywood Detective Film (State University of New York Press, 2006) and the co-edited collection The Devil Himself: Villainy in Detective Fiction and Film (Greenwood Press, 2002), as well as articles on the film versions of The Maltese Falcon, the contemporary Hollywood war film, the female film detective, the African-American film detective, and John Woo's action films. Her latest book, forthcoming in 2010, examines the history of the female detective in Hollywood film and is entitled Detecting Women: Gender and the Hollywood Detective Film.We recently spoke to her about her last book Detecting Men: Masculinity and the Hollywood Detective Film (State University of New York Press, 2006).------------You saw a gap in scholarship and decided there was a need to write Detecting Men.I have to admit that - at least initially - my interest in the genre was less professional than personal. My father had raised me on re-runs of the 1960s Perry Mason television series and my mother had introduced me to Agatha Christie; then as a teenager I discovered Hitchcock and film noir and the rest, as they say, is history. In my final year of undergrad, I took my genre theory course at the same time as a detective fiction class and also began working on masculinity in melodrama (on John Woo's action films). These research interests came together in my doctoral research when I realized that - except for film noir - the detective film as a genre had been basically overlooked by scholars. While there were some excellent tributes to the genre detailing specific characters or adaptations of detective fiction, including books by William Everson (1972), Jon Tuska (1978), and Michael Pitts (1979), there were few critical or scholarly commentaries on the genre. The one exception is Frank Krutnik's In a Lonely Street (1991) but, again, that book focuses on noir only. I was inspired to attempt a recovery of the genre beyond noir and consider the ways that the genre had evolved over the decades from the 1930s to the 2000s. I remember during my Ph.D. thinking how lucky I was to be working on something that I truly loved: my work was to watch, research, and write about my favorite genre. After completing my Ph.D., I spent a couple of years revising and expanding my thesis and the finished project was Detecting Men which was published by State University of New York Press in 2006.You state that your aim is to investigate the dominant trends or cycles that, in themselves, offer a cohesive treatment of masculinity in relation to good and evil/law and order as the hero, but in comparison with one another demonstrate shifts in social conceptions of masculinity.My years of researching the detective film have led me to the conclusion that a film's thematic concerns are determined less by a genre's conventions and more by contemporaneous social concerns. Thus, the end product of my research was to identify the major shifts in the genre and to account for those shifts by exploring the social, economic, and political context of a specific time. The recent shift in film genre criticism (see Nick Browne) has been to explore genres as products of specific socioeconomic and industrial moments rather than as a cohesive body of films over a long period of time. The detective genre as a term, then, does connote consistency over the decades as it identifies a group of texts with the common topic of the investigation of a crime and the common structure of the detective as protagonist; however, the genre is not cohesive in terms of its representation of detectives. Rather than search for generic cohesion, Detecting Men exposes the individual trends that were popular in specific decades in order to demonstrate that the thematic concerns of films are determined less by generic convention and more by socioeconomic change. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,017 | 0,012 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».