Bibliographic record
Abstract
At the risk of sounding pessimistic, I would argue that it is easier to imagine landscapes of injustice than it is to imagine landscapes of repair.This is not because injustices are more catastrophic, grandiose, or visible (they are often none of these things) as compared to repairs which can be slow, costly, and difficult.I say that landscapes of repair are difficult to conceptualize and construct because repairs are vulnerable to appropriation and injustice.To put it more clearly: in conversation, our ideas of repair are often based on returning to (what we imagine as) a previous state of pristineness or functionality.To repair can refer to something small like sewing on a button; or it can refer to bigger practices like that of mending systemic practices that discriminate or exclude -both are acts of sustainability and restoration that begin with the acknowledgement of something being broken.However, repair can also come disguised as corporate developmental narratives that are based on principles of erosion, overconsumption and extraction that closely mimic colonial practices.Repair is reconstruction, is world-building, is important.Repair is also linked with speculations of the future.Any talk of mending something, or righting the wrong, or building a better world, is based both in an acknowledgement of the wrongs of the past and a hopeful plunge into the future.The third talk of the series Landscapes of Injustice, Landscapes of Repair focused on repair through speculative fiction, future, and futurisms.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.081 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| 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".