Improving Monitoring and Evaluation in the Civic Tech Ecosystem
Bibliographic record
Abstract
For nearly a decade, civic tech stakeholders have been creating technology-supported solutions to civic challenges. Globally, the civic tech movement is rapidly professionalizing but has a limited history of documenting evidence of successes and challenges. Robust monitoring and evaluation in the civic tech ecosystem are necessary to create a foundation of knowledge for future initiatives. Monitoring plays a key role in improving services, pivoting approaches and guiding more efficient resource allocation. Evaluation highlights what is working, what is not working, and critically, why? In a sector that merges data, design and technology with user-centred principles, monitoring and evaluation in the civic tech ecosystem have several inherent challenges. This paper suggests that a theory-based evaluation approach called Contribution Analysis has the necessary sophistication and agility to support comprehensive monitoring and evaluation to support the growth and sustainability of the movement. This paper applies the early steps of contribution analysis to two Canadian civic tech projects to demonstrate its feasibility for civic tech.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".