Shame, Humiliation and Social Isolation: Missing Dimensions of Poverty and Suffering Analysis
Bibliographic record
Abstract
While people living in poverty talk about isolation, shame, and humiliation as being key aspects of their lived experiences of suffering, until recently, there has been no international data on these aspectsmaking them "missing dimensions" within poverty analysis and within research into suffering.Drawing upon international fieldwork and datasets from Chile and Chad, this chapter examines the relevance of social isolation, shame and humiliation in contexts of poverty, to research on suffering.The chapter suggests that the use of particular indicators of shame, humiliation, and social isolation can better recognize distributions of suffering.It can also help identify individuals and sub-groups within those living in multidimensional poverty -or of the general population at large -that are affected by concrete and particularly hurtful situations.Consequently, they can help to identify levels of suffering which are Mills, Zavaleta, Samuel Shame, Humiliation and Social Isolation The Oxford Poverty and Human Development Initiative (OPHI) is a research centre within the Oxford Department of International Development, Queen Elizabeth House, at the University of Oxford.Led by Sabina Alkire, OPHI aspires to build and advance a more systematic methodological and economic framework for reducing multidimensional poverty, grounded in people's experiences and values.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".