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Record W7135090282 · doi:10.1111/newe.70007

Escaping Austerity: How Abundance Can Save Progressive Governments

2025· article· en· W7135090282 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIPPR Progressive Review · 2025
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence Applications
Canadian institutionsRealNetworks (Canada)
Fundersnot available
KeywordsProsperityGovernment (linguistics)State (computer science)PoliticsFaithRevenueEconomic stagnationAction (physics)

Abstract

fetched live from OpenAlex

ABSTRACT The UK's current political stagnation stems from a lack of growth, which has forced the government into a zero‐sum "politics of austerity" and a lost tax revenue of £150 billion annually compared to US growth rates. To escape this, progressives must embrace an "abundance agenda" that prioritizes building homes, cheap energy, and modern infrastructure. This requires shifting the narrative from abstract GDP figures to tangible public benefits while aggressively distinguishing genuine innovation from corporate rent‐seeking. Furthermore, the government should adopt a "flexicurity" model to protect workers during the AI‐led industrial revolution, ensuring that technological disruption leads to shared prosperity rather than obsolescence. Ultimately, abundance is the only viable "Third Way" to fund radical state action and restore public faith in liberal democracy.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.337
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it