Defining and Demonstrating Value for Money: Strategies for Assessing the Impacts of Community Economic
Bibliographic record
Abstract
Accountability, value for money, results-based management, audits and evaluation are prominent themes in state-social economy interactions in Canada today. Community economic development organizations have been put on the defensive by the federal government’s discourse and administrative requirements associated with performance measurement. Drawing on the fields of management, public policy and program evaluation, as well as local-level case studies, this paper advances three inter-related arguments: First, there is an emerging “tool-box ” of evaluation methods and techniques that appropriately and efficiently assess the impacts of CED initiatives. Second, recent applications of these tools indicate that CED organizations and social enterprises generate significant non-financial value-added and social return on taxpayer investment, that is, blended value. Third, civil society has an opportunity now to take the offensive and gain control of the evaluation agenda. Stakeholder participation can be mobilized to define results frameworks and indicators, and demonstrate the value for money produced by the CED sector.
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 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 | 0.143 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".