Bibliographic record
Abstract
Socialforskningen i Grønland har gennem årtier dokumenteret sociale problemer. Populært sagt har socialforskningen i Grønland indtil for nylig kun bestået af beskrivende, kvantitativ elendighedsforskning. Der eksisterer således (modsat fx socialforskning med canadisk inuit) stort set ikke nogen kvalitativ eller deltagerorienteret forskning om vilkår for indsatser og praksis i forhold til at håndtere de sociale udfordringer. Der har således manglet sociologisk og handlingsorienteret praksisviden, der kan understøtte professionel og organisatorisk kapacitetsopbygning i det socialpolitiske felt. Denne artikel handler om empowerment og aktionsforskning med socialarbejdere i Grønland og bygger på Steven Arnfjords ph.d. projekt fra 2014. ENGELSK ABSTRACT: Steven Arnfjord and John Andersen: Social Work and Action Research in Greenland Years of social science research in Greenland has documented a range of social problems in the country. However social research in Greenland has been limited to quantitative research that has focused only on misery. Contrary to what we have seen in Canadian Inuit research, there has been no qualitative nor participatory research into the concrete circumstances under which Greenlandic social workers deal with the social challenges they face daily. This article draws on a research project that, for the first time, employed exploratory interviews with social workers. Analysis of these interviews uncovered the social workers’ feelings of despair and their atomised sense of loneliness because of having no references to an external network of professionals (e.g. through a union). The research was then extended to an action research project, which set out to form a social workers union in order to create a sense of unity and professional group awareness within the profession. Keywords: Greenland, action research, empowerment, social work, social planning, marxism.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".