“Ask for More Time”: Big Data Chronopolitics in the Australian Welfare Bureaucracy

Since 2016, welfare recipients in Australia have been subject to the Online Compliance Intervention (OCI), implemented through the national income support agency, Centrelink. This is a big data initiative, matching reported income to tax records to recoup welfare overpayments. The OCI proved controversial, notably for a “reverse onus,” requiring that claimants disprove debts, and for data-matching design leading frequently to incorrect debts. As algorithmic governance, the OCI directs attention to the chronopolitics of contemporary welfare bureaucracies. It outsources labor previously conducted by Centrelink to clients, compelling them to submit documentation lest debts be raised against them. It imposes an active wait against a deadline on those issued debt notifications. Belying government rhetoric about the accessibility of the digital state, the OCI demonstrates how automation exacerbates punitive welfare agendas, through transfers of time, money, and labor whose combined effects are such as to occupy the time of people experiencing poverty.

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O4E.1 Cross-walking countries’ industry classifications using concordance files compared to fuzzy data matching, to utilise an international exposure dataset

Occupational and Environmental Medicine ◽

10.1136/oem-2019-epi.106 ◽

2019 ◽

Vol 76 (Suppl 1) ◽

pp. A39.1-A39

Author(s):

Ganesh Selvaraj ◽

Michael Butchard

Keyword(s):

Big Data ◽

New Zealand ◽

Data Science ◽

Health And Safety ◽

Standard Industrial Classification ◽

Fuzzy Data ◽

Economic Activities ◽

Data Matching ◽

Industry Classification ◽

Common Domain

BackgroundCarcinogen exposure data can potentially guide the work of health and safety (H and S) regulators. This project aims to use CAREX Canada data to estimate carcinogen exposures in New Zealand industries. This requires the creation of a cross-walk between the countries’ industry classifications.MethodsAgile and big-data-science methodologies were used to construct two versions of an industry classification cross-walk from the 2006 Australian and New Zealand Standard Industrial Classification (ANZSIC06) to the Canadian version of the 2002 North American Industrial Classification (NAICS2002), used by CAREX Canada.Firstly, concordance files from government statistics bureaus cross-walked the path: ANZSIC06 ->International Standard Industrial Classification of All Economic Activities Rev4 ->NAICS2017->NAICS2012->NAICS2007->NAICS2002. The cross-walk accounted for ‘one-to-many-to-one’, non-machine formats, and missing/erroneous values.Secondly, a fuzzy data matching pipeline was designed. Data preparation removed redundant, stop, and common domain words, and lemmatised using morphological analysis (e.g. fishing to fish). Data matching used a hybrid algorithm combining ‘JaroWinkler-distance’ and a token-sort approach (i.e. ignoring the positional occurrence of words in a sentence) to match descriptions. A trial-and-error approach was used to assign weightings and concatenate the hierarchical industry classification levels to improve match accuracy. Python language was used for implementation.For each method, random samples of 50 matches were manually classified as either poor or sufficient by two people. Disagreements were discussed and consensus reached.ResultsThe concordance cross-walk sample had 52% (95% C.I. 38%–66%) sufficient matches compared to 84% (95% C.I. 74%–94%) for the fuzzy data matching pipeline cross-walk sample.ConclusionsCross-walking countries’ industry classifications using a fuzzy data matching pipeline was more accurate than using a concordance cross-walk. The pipeline is modular enough to easily include more components. This work is part of a vision to design a semantic big-data lake, enabling integration of any data relevant to H and S.

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Big Data Matching Using the Identity Correlation Approach

Proceedings of the 1st International Conference on Advanced Research Methods and Analytics ◽

10.4995/carma2016.2016.2991 ◽

2016 ◽

Author(s):

Mary Smyth ◽

Kevin McCormack

Keyword(s):

Big Data ◽

Public Service ◽

Central Government ◽

Statistical Technique ◽

Statistical Techniques ◽

Unique Identifier ◽

Individual Level ◽

Data Matching ◽

Business Survey ◽

Administrative Datasets

Abstract The Identity Correlation Approach (ICA) is a statistical technique developed for matching big data where a unique identifier does not exist. This technique was developed to match the Irish Census 2011 dataset to Central Government Administrative Datasets in order to attach a unique identifier to each individual person in the Census dataset (McCormack & Smyth, 20151). The unique identifier attached is the PPS No. (Personal Public Service No.2). By attaching the PPS No. to the Census dataset, each individual can be linked to datasets held centrally by Public Sector Organisations. This expands the range of variables for statistical analysis at individual level. Statistical techniques developed here were undertaken for a major European Structure of Earnings Survey (SES) compiled by the CSO using administrative data only, and thus eliminating the need for an expensive business survey to be conducted (NES, 20073,4,5). A description of how the Identity Correlation Approach was developed is given in this paper. Data matching results and conclusions are presented here in relation to the Structure of Earnings Survey (SES)6 results for 2011.

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The Contemporary Ethical and Privacy Issues of Smart Medical Fields

Research Anthology on Privatizing and Securing Data ◽

10.4018/978-1-7998-8954-0.ch092 ◽

2021 ◽

pp. 1899-1908

Author(s):

Victor Chang ◽

Yujie Shi ◽

Yan Zhang

Keyword(s):

Big Data ◽

Medical Diagnosis ◽

Medical Information ◽

Health Management ◽

Sensor Technology ◽

Flow Process ◽

Hospital System ◽

Ethical Problems ◽

Data Matching ◽

Privacy Issues

With the development of big data technology, such as data mining and data matching, many industries have started a revolution, including medical field. Big data not only strengthens the accuracy of medical diagnosis, but it also enhances the efficiency of the entire medical system and relevant medical staff. Additionally, with the rethinking of innovation, the application of wearable intelligent device, RFID technology and sensor technology play positive roles in promoting medical interaction between hospital system and wearer. Smart medical provides effective methods for individual health management and promotes the progress of medical information. However, there are also some inevitable ethical problems, e.g., the leakage of privacy information, which cannot be avoided to some extents. The authors recommend some suggestions to reduce the possibilities of ethical problems happened during the data flow process.

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The Pre Big Data Matching Redundancy Avoidance Algorithm with Mapreduce

Indian Journal of Science and Technology ◽

10.17485/ijst/2015/v8i33/77477 ◽

2015 ◽

Vol 8 (33) ◽

Author(s):

G. Somasekhar ◽

K. Karthikeyan

Keyword(s):

Big Data ◽

Data Matching

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Financialization and Welfare Surveillance: Regulating the Poor in Technological Times

Surveillance & Society ◽

10.24908/ss.v19i3.14244 ◽

2021 ◽

Vol 19 (3) ◽

pp. 299-316

Author(s):

Shelley Bielefeld ◽

Jenna Harb ◽

Kathryn Henne

Keyword(s):

Social Assistance ◽

Individual Responsibility ◽

Welfare Recipients ◽

Income Support ◽

Case Study Approach ◽

Organizational System ◽

Study Approach ◽

Adverse Experiences ◽

Dual Case

In light of concerns that the technologies employed by the digital welfare state exacerbate inequality and oppression, this article considers contemporary shifts in the administration of social assistance. Specifically, it examines the surveillance of recipients of government income support focusing on marginalized peoples in two jurisdictions: social security recipients subject to the Cashless Debit Card (CDC) in Australia, many of whom are Indigenous, and persons under the purview of the Lebanon One Unified Inter-Organizational System for E-Cards (LOUISE) in Lebanon, many of whom are Syrian refugees. Taken together, the cases illuminate embedded ideologies and adverse experiences associated with the financialization of social assistance and the digitization of cash. Through a dual case study approach, this analysis draws out patterns as well as contextual distinctions to illustrate how technological changes reflect financialization trends and attempt neoliberal assimilation of social welfare recipients through intensive surveillance, albeit with disparate outcomes. After considering how these dynamics play out in each case, the article concludes by reflecting on the contradictions that emerge in relation to the promises of empowerment and individual responsibility through financialized logics and technologies.

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Welfare Surveillance, Income Management and New Paternalism in Australia

Surveillance & Society ◽

10.24908/ss.v11i3.4540 ◽

2013 ◽

Vol 11 (3) ◽

pp. 272-286 ◽

Cited By ~ 15

Author(s):

Mike Dee

Keyword(s):

Welfare State ◽

Human Services ◽

Indigenous Communities ◽

Northern Territory ◽

Welfare Recipients ◽

Income Support ◽

Purchasing Decisions ◽

Socially Responsible ◽

Individual Agency ◽

The Welfare State

This article discusses the situation of income support claimants in Australia, constructed as faulty citizens and flawed welfare subjects. Many are on the receiving end of complex, multi-layered forms of surveillance aimed at securing socially responsible and compliant behaviours. In Australia, as in other Western countries, neoliberal economic regimes with their harsh and often repressive treatment of welfare recipients operate in tandem with a burgeoning and costly arsenal of CCTV and other surveillance and governance assemblages. Through a program of ‘Income Management’, initially targeting (mainly) Indigenous welfare recipients in Australia’s Northern Territory, the BasicsCard (administered by Centrelink, on behalf of the Australian Federal Government’s Department of Human Services) is one example of this welfare surveillance. The scheme operates by ‘quarantining’ a percentage of a claimant’s welfare entitlements to be spent by way of the BasicsCard on ‘approved’ items only. The BasicsCard scheme raises significant questions about whether it is possible to encourage people to take responsibility for themselves if they no longer have real control over the most important aspects of their lives. Some Indigenous communities have resisted the BasicsCard, criticising it because the imposition of income management leads to a loss of trust, dignity, and individual agency. Further, income management of individuals by the welfare state contradicts the purported aim that they become less ‘welfare dependent’ and more ‘self-reliant’. In highlighting issues around compulsory income management this paper makes a contribution to the largely under discussed area of income management and the growth of welfare surveillance, with its propensity for function creep, garnering large volumes of data on users approved (and declined) purchasing decisions, complete with dates, amounts, times and locations.

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The Contemporary Ethical and Privacy Issues of Smart Medical Fields

International Journal of Strategic Engineering ◽

10.4018/ijose.2019070104 ◽

2019 ◽

Vol 2 (2) ◽

pp. 35-43

Author(s):

Victor Chang ◽

Yujie Shi ◽

Yan Zhang

Keyword(s):

Big Data ◽

Medical Information ◽

Health Management ◽

Sensor Technology ◽

Flow Process ◽

Hospital System ◽

Ethical Problems ◽

Data Matching ◽

Privacy Issues ◽

Big Data Technology

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Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38

Author(s):

Yunfei Zhang ◽

Jincai Huang ◽

Min Deng ◽

Chi Chen ◽

Fangbin Zhou ◽

...

Keyword(s):

Big Data ◽

Contextual Information ◽

Crucial Issue ◽

Matching Method ◽

Multi Scale ◽

Data Matching ◽

Matching Pair ◽

Data Updating ◽

Different Sources ◽

Functional Solution

With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data.

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