Submission to the Senate Economics References Committee on the 2016 Census
Bibliographic record
Abstract
<em><font size="2">Executive Summary</font></em> <b>Terms of Reference</b> The Economics Reference Committee of the Senate Standing Committee on Economics of the Australian Parliament has been asked to investigate the 2016 Census, with particular reference to - the preparation, administration and management on the part of the Australian Bureau of Statistics (ABS) and the Government in the lead up to the 2016 Census; - the scope, collection, retention, security and use of data obtained in the 2016 Census; - arrangements, including contractual arrangements, in respect of the information technology aspects of the Census; - the shutting down of the Census website on the evening of 9 August 2016, the factors leading to that shutdown and the reasons given, and the support provided by government agencies, including the Australian Signals Directorate; - the response rate to the Census and factors that may have affected the response rate; - privacy concerns in respect of the 2016 Census, including the use of data linking, information security and statistical linkage keys; - Australia’s Census of Population and Housing generally, including purpose, scope, regularity and cost and benefits; - the adequacy of funding and resources to the ABS; - ministerial oversight and responsibility, and; - any related matters. This joint submission has its origins in our research, public policy work, and community engagement at the Queensland University of Technology around the Census 2016. We provided expert commentary in the media during the controversy. The topic is of interest to both intellectual property and media law, and criminal law and justice. This joint submission by two legal researchers makes the following recommendations to the inquiry into Census 2016: <b>Recommendations</b> <i>Recommendation 1</i> - In the United States of America and the European Union, there has been a history of misuse and abuse of Census data. Such history highlights the need for appropriate protection of individual anonymity, confidentiality, privacy, security, and secrecy. <i>Recommendation 2</i> - The preparation, administration and management on the part of the Australian Bureau of Statistics (ABS) and the Government in the lead up to the 2016 Census was inadequate. The privacy assessment was cursory and lacked independence and impartiality. There was a lack of genuine consultation with the public, civil society, privacy experts, and information security specialists about the 2016 Census Model. <i>Recommendation 3</i> - The Australian Bureau Of Statistics has taken an overly broad approach to the collection, retention, and use of data obtained in the 2016 Census. <i>Recommendation 4</i> - Names and addresses (and other data at a personally identifiable level) should not be collected in the Census, and any data of this nature collected be destroyed. <i>Recommendation 5</i> - The Australian Bureau of Statistics should not create Statistical Linkage Keys (SLKs). <i>Recommendation 6</i> - That previous or future Australian Bureau of Statistics data breaches are disclosed and subject to formal inquiry. <i>Recommendation 7</i> - The Australian Bureau of Statistics should stripped of the power to make threats and issue fines in light of the importance of voluntary participation, informed consent, and research ethics. <i>Recommendation 8</i> - There is a need to overhaul contractual arrangements in respect of the information technology aspects of the Census. <i>Recommendation 9</i> - That oversight and accountability processes be strengthened, either through a more active role of the Office of Australian Information Commissioner (OAIC); or the completion of an independent and transparent Privacy Impact Assessment. <i>Recommendation 10</i> - Caution must be exercised in the interpretation and use of the 2016 census data due to the issues that arose during its administration, and the subsequently lower than usual response rate. <i>Recommendation 11</i> - The Federal Parliament should implement the recommendations of the Australian Law Reform Commission in respect of the introduction of a statutory cause of action for serious invasions of privacy. <i>Recommendation 12</i> - It is worthwhile comparing the experience of the 2016 Census in Australia with the 2016 Census in Canada. While Canada enjoyed a high completion rate of its census, Statistics Canada did experience significant problems in respect of information technology. Moreover, there have been conflicting legal precedents in Canada about the imposition of fines for non-completion of the mandatory census.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".