Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".