THE TALE OF SOULMATES OR A MARRIAGE OF CONVENIENCE? TYING THE KNOT OF HUMAN RIGHTS WITH DEVELOPMENT AND ITS GOALS THROUGH DONOR PROGRAMS AND PROJECTS. THE CASE OF THE CANADIAN INTERNATIONAL DEVELOPMENT AGENCY (CIDA)
Bibliographic record
Abstract
The purpose of the dissertation was to examine whether globally agreed development goals (Millennium Development Goals (MDGs), with a specific focus on poverty reduction) were operationalized in human rights, access to justice and rule of law programs/projects of Canadian International Development Agency (CIDA) and of its executing partners. The analysis of the CIDA’s reports to Parliament and programing documents indicates that from the first years of the launch of MDGs, they were operationalized within the CIDA’s programing architecture and remained as a macro- level goal of the agency. MDGs, with the focus on poverty reduction, were treated as an ultimate goal, towards which the issues within the democratic governance portfolio were also geared. Though CIDA acknowledged that human rights were not explicitly mentioned in MDGs, in its programming documents CIDA continuously linked MDGs with human rights considerations. CIDA’s programing also envisioned the achievement of poverty reduction through activities which focused on human rights, rule of law, legal and judicial system.\nDespite the fact MDGs were declared as the overarching aim of CIDA’s efforts, documents of the analyzed CIDA funded projects did not reference MDGs within their projects’ architectures. Neither projects’ goals nor outcomes indicated that they were explicitly contributing to reaching MDGs. While not explicitly referring to MDGs, some projects stated their intent to contribute to poverty reduction and/or assistance to the poor and marginalized. Even though these projects were concerned with poverty reduction and/or interest of the poor and vulnerable groups, the silence towards the MGDs can be interpreted as a gap between the CIDA’s corporate declared development agenda and goals of the projects implemented in the field.\nThe conclusions are based on the analysis of Government of Canada policy papers, CIDA’s official policy and strategy papers on democratic governance, human rights, poverty reduction and sustainable development, and CIDA’s reports to Parliament. As a part of the data collection, interviews were conducted with CIDA’s current and former staff, as well as professionals who worked for organizations which implemented CIDA financed projects. Documents analyzed in the dissertation projects were obtained through access to information requests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".