Constructing promissory futures to defer moral scrutiny: The dilemma of healthcare austerity
Bibliographic record
Abstract
How can actors use the future to politically navigate moral disputes today? This article examines how projected futures are constructed and mobilised to suspend present-day moral dilemmas. Utilising the Economies of Worth and Barbara Adam’s sociology of time, we discursively analyse the moral dilemma between civic virtues and financial savings in UK healthcare austerity (2010–2018). This reveals how the pro-austerity government avoided moral scrutiny of their posited solutions to apparently intractable moral struggles using future projections we term ‘promissory futures’. Promissory futures project desirable futures that ambiguously seem both secured enough to be reliable, and open enough to escape today’s moral dilemmas. Thus, government could use them to shift the temporal focus away from present-day moral critique of how they were balancing austerity’s financial savings against civic virtues, and into a future where savings and civic virtues were compatible. However, promissory futures contain a contradiction: the future cannot be both already-secured and still-open. Thus, critics could eventually deconstruct promissory futures, requiring government to repeatedly reconstruct them. There thus emerges less a definitive moral settle- ment and more a continual process of moral settl- ing, whereby a series of promissory futures together forestall critique of underlying settlements, thus delaying moral struggles’ denouements.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".