Bibliographic record
Abstract
The interview took place on November 17, 2019, at Su Friedrich’s home in Brooklyn, New York. Let’s start at the beginning. How did you become a filmmaker? I graduated from Oberlin College with a degree in studio art and art history in 1975. I had primarily studied photography as the studio part of my major. I moved to New York City in 1976, and for two years I was doing photojournalism and studio photography, but then I attended a three-night Super 8 mm workshop with David Lee at the Millennium Film Workshop. Dave had studied at SUNY Buffalo [State University of New York Buffalo] with Hollis Frampton. I was also seeing films that were interesting to me, European art cinema—for example, films by Rainer Werner Fassbinder—at the Bleecker Street Cinema. It was exciting to me, but I didn’t consider going to film school, partly because I had been in school long enough but mainly because it was incredibly expensive, and I had no money. I became a filmmaker when I realized that it was much more interesting to make films than to do photography because one could work with movement, rhythm, and multiple images. But it was a difficult decision because I had a nice camera, and I had built a darkroom in the I was at the I had a in photography, and I also The Millennium was a because it was could a for a a and the for a were I didn’t to film Dave and I became with and for a of were was also years than me, and in my as I to make you as part of a at the The that I was going to the Millennium the to and to do my and that I was in I going to the with by were and had a of I as a I became with that much than me, but had also to SUNY It much a of but that I was with had a film had to make and I from films and I and that more and films the on my work because I also a and films were films were and Hollis and the years in of my and then moved been the for my the I also of and a it to make a The that I films by when I was going to the Bleecker Street that was a filmmaker was in and it that the of film that I But I also the was films in and I had to make a I was a of the that the art on and The of and a were my It was for as a had to New York to become a and filmmaker to as had were and at the I had a with one of because I had been doing a of work for the I that I to work on my but I was much on and a you do the you to do and you It was to that to you film did you a of been a I mainly studied studio art when I was in my degree was in art but Oberlin had a art history I the of been my example, in a a a and that the of the I I that to the of But I had a of the of I could a of were I didn’t the been the for as a filmmaker my as Su the filmmaker and then as Su the work it to because of of a it But for it it to I that I did a of on history and the that were by as as by and I also as much as I could the been to in I was in the work by was and it a on I I that were at the for by the film because I to I could I doing than in the films that my But I didn’t a of the more that was been much of for you of and the that the in I a that when the of of the I the and a that it the of The I was a film a a I 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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".