Outcome Mapping: Documenting Process in the Métis Settlements Life Skills Journey Project
Bibliographic record
Abstract
Mapping serves as a metaphor for where we are now, where we have been, and where we are going. In this paper the authors illustrate the use of outcome mapping as a methodological framework for documenting the planning, monitoring, and evaluation process for the Métis Settlements Life Skills Journey (MSLSJ) project. The MSLSJ is a multi-year, multi-site, multi-method research project. It is centered on building relationships and facilitating knowledge exchange between the University of Alberta team, Métis Settlement Councils and administrators, and Settlement members. We highlight how the outcome mapping framework enables us to document project processes through the identification of key boundary partners and strategies in support of learning. Outcome mapping became a reflective and strategic tool for the MSLSJ project, reflecting on six years of data from seven sites, representing over 430 participants, and guiding the project forward.
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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 | 0.041 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".