The Kinomatics Australian Film Production Dataset
Bibliographic record
Abstract
This is the public repository for the Kinomatics Australian Film Production Dataset, a large, curated dataset describing Australian feature film production and personnel from 1975 to 2022. Here, we briefly describe the contents of this repository, which are explained in more detail in the dataset's technical documentation. Repository contents Data files There are two main data files that form this dataset. The first is films.csv, a table where each row corresponds to a unique film in the dataset, and the columns contain variables describing those films. The second is roles.csv, a table where each row corresponds to an instance of a person filling a role on a film, and the columns contain variables describing that role (including identifiers for the film and the person). Documentation The technical documentation of the dataset is contained in the technical_documentation.odt file. Here, we provide a detailed account of the data collection, validation and preparation processes. We also provide tables describing each column in each of the data files. We have also written a research data paper to accompany the dataset, which focuses less on the technical details and more on motivating the dataset itself and the decisions we made in deciding its scope and coverage. This data paper has been submitted for peer review, and a copy of the initially submitted manuscript can be found in the file preprint.pdf. Change log The file changelog.pdf documents changes between released versions of the dataset. Issues For any issues related to the dataset or this repository, please contact either of the lead authors Pete Jones (pete@petejon.es) or Deb Verhoeven (deb.verhoeven@ualberta.ca) and let us know what the problem is and how we can fix it.
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 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 | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.044 |
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".