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Enregistrement W2998581065 · doi:10.4324/9781315637167-27

Affective labor and the work of film festival programming

2016· article· en· W2998581065 sur OpenAlexaboutno aff
Liz Czach

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueCinema and Media Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProgrammerDreamFilm festivalVisual artsWork (physics)Media studiesSociologyAdvertisingArtPsychologyComputer scienceEngineeringBusiness

Résumé

récupéré en direct d'OpenAlex

From 1995 to 2005 I was a film programmer at the Toronto International Film Festival (TIFF )—one of the most prestigious film festivals in the world. Along with a co-programmer or two, I would help select the Canadian films that would screen at the festival that year. In many respects this was a dream job. I got paid to watch movies and discuss them with my film-obsessed colleagues. I met filmmakers, producers, actors, and other members of the creative teams. I traveled to other festivals and cities to preview films. My programming decisions and the films I advocated helped shape national film culture. During the festival I introduced films and facilitated question-andanswer periods, I attended parties and dinners, and I accompanied celebrities down the red carpet. The months of hard preparatory work melted away in the euphoria of those fast-paced adrenaline-filled ten days; it was all very exciting and yes, at times, glamorous. Programming is one of the most desirable and sought-after positions at a festival. Given the idealization of programming as an occupation, it is unsurprising that during the 11 years I worked at TIFF I was frequently asked by volunteers, interns, junior staff, programming assistants, and others how I became a programmer, as many of them yearned for the opportunity to do the same. More than a decade after leaving the festival I am still asked if I miss working there. And in some respects, I do. As an unrepentant cinephile who will watch almost anything (a good quality for a film programmer) I loved being able to screen hundreds of films and see what young as well as seasoned filmmakers were up to. It was an amazing privilege to have an insider’s view on the film productions of the last year and to meet so many talented and interesting people. There is little doubt that film programming can be an exciting and fulfilling job, but the romanticized view of programming as hobnobbing with celebrities and leisurely screening films obfuscates the fact that despite all the perks and privileges, it is still a job. In this chapter I propose that one productive way to understand the work of film programming is as a form of affective labor: examining the positive forms of affect that festival work can entail-that is, the pleasure and excitement experienced during the festival-alongside the lesser-known affective states of despair, disappointment, and anger that need to be managed as aconsequence of films being rejected from the festival. Employing Carolyn Ellis’ understanding of autoethnography as “research, writing, story, and method that connect the autobiographical and personal to the cultural, social, and political” (Ellis 2004: xix), I draw upon my experience of working at TIFF as an autoethnographic case study to examine film programming as affective labor. Beginning in the 2000s, critical labor scholars have examined working in the creative industries to argue for the immaterial, affective, and precarious aspects of creative labor. Groundbreaking work such as Mark Deuze’s Media Work (2007) as well as David Hesmondhalgh and Sarah Baker’s Creative Labour (2011) have investigated the emotional, financial, and physical toll that working a precarious dream job can have on cultural workers. Their studies have primarily addressed workers in the music, film, and television production industries, but their findings correlate strongly with my experiences working as a film programmer. Skadi Loist has addressed the precarious nature of festival work arguing that:Despite the (supposedly) prestigious status of film festival labour, most people working for festivals find themselves in insecure working conditions. The festival organizations are often precarious entities themselves, struggling for funding and usually operating on a bare minimum, with only very few full-time and year-round employees, some seasonal staff, in low-pay or entry-level positions, and supported by interns and volunteers. This is true for most festivals (even at A-list events such as Berlin, Cannes and Venice).

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,514
Score d'incertitude au seuil0,099

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,207
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2016
Routes d'admission1
Résumé présentoui

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