Our curious silence about kindness in planning: Challenges of addressing vulnerability and suffering
Bibliographic record
Abstract
Discussions of “justice” in planning are commonplace; discussions of “kindness,” strangely enough, are rare. Perhaps not by accident. Taking “compassion” as an empathetic, intentional orientation toward suffering, we analyze “kindness” as the situated action of compassion that requires—following and extending analysis of Martha Nussbaum—four contingent, contextually sensitive practical judgments: (1) empathetic recognition of another’s vulnerability or suffering; (2) causal/moral gauging of the sources of that vulnerability or suffering; (3) crafting of acts to mitigate that vulnerability/suffering, and (4) forming the motivation to respond practically to that Other’s situation. Diverse planning cases from Cleveland, the Canadian Yukon, and Australia illuminate these practical judgments. We show how these contingent judgments can go wrong and thereby produce not kindness but humiliation, shame and victim blaming, pity and condescension, or dependency not autonomy. In doing so, the article makes a fresh contribution toward analyzing the moral requirements of, and the risks faced in, any planning seeking to respond to others’ vulnerabilities and suffering.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.105 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".