TRACING HOW GOVERNANCE SHAPES AND LIMITS APPROACHES TO POVERTY REDUCTION, THE INFLUENCE OF FUNDERS: A CASE STUDY OF TWO POVERTY REDUCTION ORGANIZATIONS.
Bibliographic record
Abstract
Supervisory Committee Dr. Catherine McGregor, Department of Psychology and Leadership Studies Supervisor Communities throughout Canada are organizing to find ways to support people living in poverty and remove the barriers that create or keep people living in marginal conditions. There appears to be no “right’ way to create an organizational structure that is effective; communities take a variety of approaches and are inventing or adapting models to meet local needs. With limited funding dollars available, funders involvement in the governance and decision-making of organizations that they also fund raises the question of how their involvement constraints or enhances the organization. This report seeks to document the role of funders through the study of two poverty reduction organizations in neighbouring Lower Mainland municipalities. The process of data collection and what was available and not available publicly led to new areas of inquiry. The data collected demonstrated that the presence of funders and their leadership helped in bringing “leaders” to a community table to reduce poverty. However, it does appear that the organization with the least amount of funding had a model of governance and action, which allowed them to use their small amount of dollars to leverage them to achieve a great number of projects. The influence of funders can be traced to the language used and professionalization of communication in the one organization and possibly to the less open and public distribution of information. A surprising element was the apparent lack of promotion by the funders of their commitment and support to the initiative that they are heavily invested in. Evidence from both organizations indicates that the issue of power and powerlessness has been discussed in their desire to provide an inclusive environment.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".