Australian State MPs in their electorates: constituency work and the evolution of representation
Bibliographic record
Abstract
Members of Parliament (MPs) in Australia and other established democracies work long hours in their electorates, assisting constituents with service problems, discussing policy issues, interacting with community groups, and connecting with members of their political parties. Despite their hard work, elected representatives and their political parties are increasingly perceived as disconnected from everyday people. Research and commentary suggests that the work MPs do in their constituencies is contributing to this problem. However, existing studies of Australian constituency work lack detailed information on the representative practices, logics, and tensions which guide MPs’ efforts. Moreover, this body of research is concentrated at the federal rather than state level. To further our understanding, this thesis answers the question of what state MPs do in their electorates and why, utilising an analytical framework which sees constituency work as an exercise in connection building with important policy, service, symbolic, and partisan functions. I answer these questions by adapting and expanding upon this framework in two ways. First, I examine how patterns of connection building have changed since the 1890s as MPs’ constituency work has increased in complexity and volume in the United Kingdom, Canada, and Australia. Thereafter, to understand contemporary practices of constituency representation, I draw upon a novel nationwide survey of 111 state and federal MPs, and 36 interviews at all three levels of government in Queensland, conducted during the 2019-2021 period. Amidst frequent natural disasters, persistent distrust in politicians and a rising tide of service requests, I find MPs building connections in a variety of distinct styles, while favouring pragmatic over transformative approaches to policy. Furthermore, while MPs seek to build trust by demonstrating that they are responsive and ‘not like other politicians’, these efforts are in tension with their role as ‘authentic’ and effective party representatives. I find possibilities within MPs’ current practices that the represented can be brought into more active participation in the political process. This is important because while healthy scepticism is an important part of a functioning democracy, if the public’s perceptions of representative politics are inaccurate and overly negative, scepticism may give way to a corrosive and disempowering cynicism
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".