Articles The structure of poverty: a challenge for the training of social workers in the North and
Bibliographic record
Abstract
Social work is a profession that operates in almost every society across the world; and every day hundreds or thousands of social workers in each of these societies are dealing with some aspect or manifestation of poverty. In this context it would be appropriate to ask whether or not social workers are properly trained to understand the nature of poverty, to treat its consequences, and also- as far as lies in their capacity- deal with its causes. Generally speaking, does their training teach them about the significance of socioeconomic factors in the lives of their clients? This article will attempt to answer these questions in relation to countries of the economically developed North and those of the developing South. First, a brief profile will be given of social service clients and of poverty in Canada, assuming Canada to be more or less representative of Northern developed countries. This will be followed with an examination of the socioeconomic factors in practice and in the training of social workers. In the second part, and as a comparison, the article will turn to poverty in the developing countries of the South and consider aspects of social work training and practice in that particular context with major reference to Latin America with which the authors are most familiar.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".