THEORIES OF EVALUATION AND THE MEANING OF A SUCCESSFUL AFCC PROGRAM
Bibliographic record
Abstract
This presentation aims to describe the experience of the Age-Friendly Cities and Communities in Quebec, Canada (AFCC-QC), in order to contribute to knowledge building related to the evaluation process and to reflect on the pattern of evidence of what could mean a successful AFCC program regarding to different contexts. AFC-QC started with 7 pilot projects in 2008 and is now in implementation in 766 municipalities in 2016. It’s based on a mixed methods design, which provides an important body of data. This experience raises the question of how do we evaluate an AFCC program? There are more than a dozen of affiliated programs in the WHO Global Network of Age Friendly Cities and Communities (GNAFCC). Each of them takes place in different contexts. The theory of evaluation states explicitly the importance of these contexts. Through the lens of three different types of evaluation models (experimental, logic model, participatory), we’ll discuss how these models can or cannot address the different realities of AFCC.
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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 | 0.052 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.086 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".