A new, comprehensive database of all proceedings of the Australian Parliamentary Debates (1998-2022)
Bibliographic record
Abstract
This database contains data on the proceedings from each sitting day in the Australian Parliament by the House of Representatives from 02 March 1998 to 08 September 2022, in both CSV and parquet forms. These data were parsed entirely from the XML Hansard transcripts available on the Australian Parliament website. The database is organized as follows: hansard-daily-csv.zip contains all individual Hansard sitting day files in CSV form. hansard-daily-parquet.zip contains all individual Hansard sitting day files in parquet form. hansard-corpus.zip contains the full Hansard corpus in CSV form and in parquet form. hansard-code.zip contains all the R files we used to build our database, and any necessary CSV files to run those R scripts. The README.md file in this folder contains a detailed description of each script, outlines our workflow, and provides some example R code for users of our database. hansard-supplementary-data.zip contains data on Hansard debate topics, and data on divisions in the House that were transcribed during our time frame. This folder also contains the CSV we used to correctly map PartyFacts IDs to the party abbreviations found in our database.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.050 |
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".