Canada in Sudan, Sudan in Canada
Bibliographic record
Abstract
Presenting field work conducted by fourteen Canadian and Sudanese-born Canadian researchers between 2003 and 2011, Canada in Sudan, Sudan in Canada explores salient and timely issues faced by both countries. Sudanese immigration to Canada and the transnational ties between the two countries are illuminated in the context of various case studies. Tensions, both social and political, are discussed through the recent secession of South Sudan, the Darfur conflict, and the rise of Islamic fundamentalism. The authors also broach the reconstruction efforts in education and health initiatives, transnationalism from below, and Canada’s role in conflict resolution in Sudan. Using qualitative and quantitative research methods that include interviews, surveys, participant observations, discourse analyses, and document analyses, researchers from a wide range of disciplinary approaches - sociology, anthropology, political science, social work, and health studies - reveal important conceptual and empirical perspectives about the processes of inclusion and exclusion. At a time when the Sudanese diaspora in Canada is growing and the conflict in Sudan has become a preoccupation of the international community, Canada in Sudan, Sudan in Canada reveals the root causes of conflict in Sudan and identifies measures to foster peace, stability, and development. Contributors include John Clayton (Samaritan’s Purse Canada in Calgary), Rod Crutcher (University of Calgary), Dalal Daoud (PhD student, Queens University), Allison Dennis (University of Calgary), Martha Fanjoy (University of Calgary), Juli Finlay (University of Calgary),, Amal Madibbo (University of Calgary), Susan McGrath (York University), Ruth Parent (University of Calgary), Shelley Ross (University of Alberta), Scott Shannon (University of Calgary), Ali Kamal, Ashley Soleski, and Daniel Madit Thon Duop (IMA World Health).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.041 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".