Poverty (Number 1 Goal of the SDG) of Disabled People through Disability Studies and Ability Studies Lenses: A Scoping Review
Bibliographic record
Abstract
According to the World Bank, the world will not meet the SDG of ending extreme poverty in 2030. Disabled people live disproportionally below the poverty line. Many societal developments and discussions can influence the poverty level of disabled people. This study aimed to better understand the academic engagement with the poverty of disabled people in general and in Canada. To fulfill this aim, we performed a scoping review of academic abstracts obtained from SCOPUS, the 70 databases of EBSCO-HOST, and Web of Science. We performed a frequency count and a content analysis of abstracts containing the terms “poverty” or “impoverish*” or “socioeconomic” or “SES” or “income”. We ascertained how the abstracts engaged with the poverty of disabled people in general and in Canada and in conjunction with keywords linked to a select set of societal developments and discussions we saw as impacting poverty and being impacted by poverty. We also looked at the use of concepts coined to discuss ability judgments and social problems with being occupied, two areas that impact the poverty of disabled people. We found that disabled people were mentioned in 0.86% of the abstracts using the term “poverty” in general and 4.1% (88 abstracts) for Canada. For the terms “impoverish*”, “socioeconomic”, “SES”, and “income”, the numbers were 3.15% in general and 0.94% for Canada. The poverty of disabled people who also belong to other marginalized groups was rarely covered. Our qualitative content analysis revealed that many of the hit-count positive abstracts did not cover the poverty of disabled people. We found 22 relevant abstracts that covered the poverty of disabled people in conjunction with technologies, eight in conjunction with accessibility not already mentioned under technology, eight with intersectionality, seven with “activis*” or advocacy, three with sustainability, two with climate change, and none for burnout or ally. The occupation and ability judgment-focused concepts were rarely or not at all employed to discuss the poverty of disabled people. Our findings suggest many gaps in the coverage of the poverty of disabled people that need to be fixed.
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 | 0.026 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.033 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".