Who is Telling ‘Australian’ Stories? The Results from the First Nations and People of Colour Writers Count
Bibliographic record
Abstract
The Australian Publishing Industry has long been critiqued for its lack of diverse voices. In Australia movements such as Voices From the Intersection and The Stella Diversity Survey have been aimed at bringing an awareness of this lack to a larger audience, while festivals such as Blak and Bright, and awards such as The Next Chapter, have sought to highlight the works of authors who identify as First Nations or as Writers of Colour. The study discussed in this paper is the first large-scale study that sought to identify how culturally diverse the author cohort was in the study year of 2018. The First Nations and People of Colour Writers Count (henceforth FNPOC Writers Count) sought to identify the publication rate of books in 2018 that were by Australian authors who publicly identified as First Nations people or People of Colour. The purpose of the project was to develop the first large-scale numerical dataset that illustrated the inequity in Australia’s publishing industry that has been anecdotally observed for many years.
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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 | 0.008 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".