The Kinomatics Australian Film Production Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents a novel, extensive, and thoroughly documented dataset describing Australian feature films and the personnel filling ten key production roles on those films. The dataset is curated from public information in multiple sources and draws on further supplemental resources to verify, validate and consolidate this information. In total, the data describes 22,720 roles filled by 9,397 distinct people across 1,877 films, covering an important 47-year period in the Australian film industry. The authors outline how the dataset solves several problems for scholars interested in data that provides a historical record of the collaborative filmmaking process. In particular, to address concerns about known coverage problems with popular sources such as the Internet Movie Database, this dataset has undergone extensive manual checking to ensure that it is reliable as a source of information on a national film industry. Moreover, the authors have carefully and manually linked each person appearing in the dataset, which allows the dataset to provide a rich source of information for exploring the relationality of filmmaking collaborations. The inclusion of ten key filmmaking roles further expands the utility of the dataset beyond existing datasets which tend to focus on actors and/or directors, writers and producers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it