Neoliberal Numbers: Calculation and Hybridization in Australian and Canadian Official Statistics
Bibliographic record
Abstract
Numbers dominate contemporary governance. Governments are expected to set and meet quantitative targets in all major policy areas. Accountability is now closely tied to performance measurement and auditing. Governmental achievements are persistently ranked against global standards and ''best practices." Political fortunes and policy proposals are constantly assessed-via polls, while programs are increasingly systemically evaluated using econometric techniques. Key ''official statistics'' such as unemployment and inflation rates are monitored by international financial markets, which can rise and fall dramatically in response to changes in the value of these indicators. As a result of this apparent hegemony of quantification, some scholars argue that calculation is a defining feature of neoliberal rule (Haggerty, 2001a; Rydin, 2007; Sokhi-Bulley, 2011). Calculative practices such as ''numeration," ''quantification," and ''datafication'' (Mayer-Schonberger & Cukier, 2013) appear to facilitate key reform agendas associated with neoliberal rationalities of government, including the increasing moves to calculate the worth of public goods and services in financial terms, along with the.seemingly relentless push to ration government programs, make their administration more efficient, and introduce market principles into public service provision.
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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 | 0.044 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.037 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".