Creating Systemic Design-Informed Impact Evaluation Frameworks: A case study with the Gord Downie & Chanie Wenjack Fund
Bibliographic record
Abstract
In this presentation, we share the development of a theory of systemic change (ToSC) with the Gord Downie & Chanie Wenjack Fund (DWF). The DWF works to build cultural understanding and create a path toward reconciliation between Indigenous and non-Indigenous peoples. In this case, a ToSC was developed to amalgamate and synthesise the work of multiple previous approaches to evaluation at the DWF. The DWF’s existing Theory of Change provided a limited appreciation of the complexity of pathways from programming to outcomes, making it difficult to describe the activities of the DWF in an evaluable fashion. Through data collection and surveys, a ToSC was created in a process that also resulted in the streamlining of objectives from 22 to 11. The resulting ToSC rendered the organisation’s theory of the system and its programs in pragmatic detail, allowing evaluators to create a systemically informed impact evaluation framework in the form of specific questions and data collection to guide program design and evaluation. Moreover, the ToSC exposed the interwoven connections and logic between the DWF’s programs, allowing for organisation-wide insights and strategy decisions that were previously unavailable. We end this presentation with a discussion of the benefits and limitations of this methodology and some recommendations for future evaluators interested in using ToSC.
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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 | 0.021 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".