'TaxTrack': Introducing a Democratic Innovation for Taxation
Bibliographic record
Abstract
In this article we introduce an input-oriented democratic innovation-that we term 'TaxTrack'-which offers individual taxpayers the means to engage with their political economies in three ways. After joining the TaxTrack program, an individual can: (1) see and understand how much, and what types, of taxes they have contributed, (2) see and understand how their tax contributions are, or have been, used, and (3) control what their tax contributions can, or cannot, be spent on. We explain this democratic innovation in two ways. The first is through evocation to prefigure what the innovation could look like in future practise which raises the prospects for both good and problematic outcomes. The second is through formal theory to produce a detailed model of the innovation to assist theory building. We conclude by discussing three interactive outcomes of 'TaxTrack' through the democratic innovations literature to establish the beginnings of a theory for the model. This theory tells us that 'TaxTrack' can return benefits to its users and the democratic regimes in which they are located but it may also place restrictions on output-oriented innovations like Participatory Budgeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".