MétaCan
Menu
← Back to cohort
Record W4391864075 · doi:10.51644/9780889208186-001

With Gratitude

2017· book-chapter· en· W4391864075 on OpenAlexaffabout

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGratitudePsychologySocial psychology

Abstract

fetched live from OpenAlex

With GratitudeI owe a great deal to many people.In the spring of 1989, when I was under attack from all quarters for using images of the body in my films, the organizers of the Toronto International Avant-garde Film Congress invited me to present a program of films by other filmmakers that incorporate similar images.This opportunity required me to reflect on the topic that has become the subject of this book.The Congress also provided, in the form of one its panel discussions, an occasion to engage with Carolee Schneemann, Birgit Hein, and Christine Noll Brinckmann, in considering the topic of this book.My presentation for that panel was the impetus that first motivated me to think about several of the issues this book raises.The enthusiasm for the subject these three women filmmakers displayed became, over time, as great a motivator, as I have often thought back to the event and the delight it gave me.I owe much to all three.In the fall of 1989, the Art Gallery of Ontario, and its film programmer at the time, Catherine Jonasson, contracted with me to present several evenings of films on the body at that institution.These evenings allowed me to understand better the scope of this project.The Art Gallery of Ontario also sponsored a short catalogue for the exhibition; because I formulated several of the ideas stated in this book in writing the catalogue, I am much indebted to that institution.Another occasion that same fall provided more opportunity to work out further ideas on the subject, as J.M. Snyder, then chair of the Department of Film and Photography of Ryerson Polytechnical University, invited me to give a lecture on images of the body that I had made for my own films; this activity was supported through

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.120
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0110.006
Open science0.0020.006
Research integrity0.0020.011
Insufficient payload (model declined to judge)0.1200.128

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.

Opus teacher head0.045
GPT teacher head0.210
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicCinema and Media Studies→French-language works237,207→