Bibliographic record
Abstract
This special issue belongs to a series of activities under the umbrella denomination “Studying and Exploring the Intersections of Fashion, Film, and Media Studies,” created in 2014 by film scholar Anne Bachmann and I. Our goal was to promote an interdisciplinary perspective to the study of fashion, film, and media. This venture was launched with two activities at the 2015 edition of the annual conference of the Society for Cinema and Media Studies, in Montreal. The first activity consisted of a panel featuring the on-going projects of four Ph.D. students working with these combined fields.[1] The second activity consisted of a workshop, in which presentations opened to discussions addressing how the use of archival material and film fan magazines, combined with film studies’ methodological approach to history, could benefit fashion research.[2] This workshop expanded into a Symposium at Stockholm University featuring established scholars who pioneered research in these fields of studies combined. This special issue of Networking Knowledge seeks to include early career researchers in such conversation, broadening the network of scholars and the combined field of expertise. Since its inception, a historical approach has been encouraged by the founders of this project. Yet, the semiotic roots used for textual analysis of costume design shall not be overlooked. In this sense, this special issue intends to present a panorama of the heterogeneous nature of studies in these interconnected fields. [1] The panel was titled “Industry Crossovers: Key Women in Fashion, Film, and Media,” with Michelle Tolini Finamore as respondent, SCMS Conference, Montreal. [2] The workshop featured presentations by Tamar Jeffers McDonald, Jenny Romero, and Elizabeth Castaldo Lundén. Because Fashion Matters: Studying the Intersections of Fashion, Film, and Media, SCMS Conference, Montreal, 29th March 2015.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.565 | 0.408 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".