Bibliographic record
Abstract
To understand what kind of collective healing practices might be most effective following mass atrocity, we need to comprehend better what counts as collective healing, and in what ways group healing processes differ from individual ones. We need clear and well-argued answers to these conceptual questions as a basis for deriving the criteria by which we might evaluate various practices in different contexts. Because means are valuable only in relation to ends, judging their effectiveness requires a definition of the ends in question and what is good about them. So, what counts as a good collective healing process? This conceptual paper proposes that the concept of healing requires that of being wounded, which in turn requires the idea that some agent performed dehumanizing actions. It identifies dehumanization as a serious form of harm, and characterizes the nature of healing processes based on this analysis. It then describes the group nature of mass atrocities. These four points enable us to separate different kinds of healing processes that are normally conflated and begin to provide a framework for evaluating diverse collective healing processes.
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 | 0.020 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".