The Expectation of Goodwill
Bibliographic record
Abstract
This chapter addresses, as a first component of the proposed framework, the first constituent expectation of trust in the citizen-government relationship: goodwill. It defines the expectation as consisting of two sub-expectations: an expectation of procedural fairness – which includes elements of transparency, citizen participation and respect for citizens’ right to equality – and an ‘expectation of good intentions’, which translates into an expectation that the elected branches’ staff will not act intransigently in exercising their control over social goods and services. The chapter also details how the courts can enforce the expectation. It explains that for this component, the courts, first, demand a fair decision-making procedure from the elected branches, and, secondly, respond to government intransigence by escalating to progressively less trusting judicial interventions. The chapter uses cases from various jurisdictions, including Canada, Colombia, Germany, Kenya, South Africa and the UK, to illustrate.
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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 | 0.001 | 0.001 |
| 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".