Bibliographic record
Abstract
The focus of this essay is the analysis of daily objects as signs in films. Objects from everyday life acquire several functions in films: they can be solely used as scene objects or to support a particular film style. Other objects are specially chosen to translate a character’s interior state of mind or the filmmaker’s aesthetical or ethical commitment to narrative concepts. In order to understand such functions and commitments, we developed a methodology for film analysis which focuses on the objects. Object interpretation, as the starting point of film analysis, is not a new approach. For instance, French film critic André Bazin proposed that use of object interpretation in the 1950s. Similarly, German film theorist Siegfried Kracauer stated it in the 1960s. However, there is currently no existing analytical model to use when engaging in object interpretation in film. This methodology searches for the most representative objects in films which involves both quantitative and qualitative analysis; we consider the number of times each object appears in a film (quantitative analysis) as well as the context of their appearance, i.e. the type of shot used and how that creates either a larger or smaller relevance and/or expressiveness (qualitative analysis). In addition to the criteria of relevance and expressiveness, we also analyze the functionality of an object by exploring details and specifying the role various objects play in films. This research was developed at Concordia University, Montreal, Canada and was supported by the Foreign Affairs and International Trade, Canada (DFAIT).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".