Bibliographic record
Abstract
The past is present in all future making activities. However, there is more that futuring processes can do to engage with past-present relationships, namely by bringing to the fore frameworks of reparation and redress. This article explores how ideas of reparative action may offer generative resources for Futures Studies. It suggests that in order to create futures characterised by justice it is essential to listen to and engage with ongoing histories of repression, violence and domination and find ways to talk about the past that support individuals, communities and nations to reimagine and remake social relations that are just and inclusive. The article explores reparative futures as they are negotiated in practice, through the lens of their pedagogical potential and ethical demands, and as world-making political possibilities. In doing so, it highlights the necessity for enhanced dialogue between Future Studies and the ‘reparative turn’ within the humanities and social sciences. We explore the tensions and unresolved questions of reparative futures along with the possibilities for future-making practices characterised by justice, care, creativity and humility for humans and nonhumans.
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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".