Bibliographic record
Abstract
Film and media practitioners and educators have been expanding the use of digital through new experiences with unusual and innovative technical and artistic “approaches.” Likewise, researchers and academics are questioning and analyzing these new practices that increasingly dominate global society, as seen in the past months with the advent of the worldwide pandemic. In 2013, we created the IFM-Interactive Film and Media Conference to provide an inclusive educational space within the digital theory and interactive studies where researchers and practitioners could discuss and present their research and work in film and media. With this purpose, the IFM has partnered with universities worldwide and established a space for a global integration between academia and the audiovisual production community that aims to forge a valuable exchange between researchers, faculty, students, practitioners, and the community. The goal is to generate a broad debate, emphasizing the need to evaluate the increasing use of digital screens in contemporary society and how people respond artistically, socially, and politically to the challenges of the digital cultural space. The work of professors, researchers, and practitioners (artists, filmmakers, and videomakers) from various areas and several countries, including Italy, Brazil, England, Spain, Canada, New Zealand, Portugal, Scotland, Germany, and the United States, constitutes this special issue with selected articles and audiovisuals from the #IFM2014. The aim is to launch IFM Journal first issues while archiving our preliminary works.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.048 |
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".