Deliverology in Community Economic Development: Enhancing Results Frameworks and Performance Measurement
Bibliographic record
Abstract
This paper will assess the viability of implementing an alternative delivery model and performance measurement framework – commonly known as ‘deliverology’ – at the level of community economic development. First, a review of relevant literature on traditional economic development delivery models and performance metrics is conducted to determine strengths and weaknesses. Next, a deliverology approach is defined and analyzed to determine whether such a model can address the weaknesses of more traditional approaches. The results indicate that a deliverology approach has many potential advantages for economic development service delivery and addresses many of the weaknesses of current models and frameworks. Since deliverology remains rather new compared to more traditional approaches, further research in terms of a case study in a large urban municipality is recommended as a way to test the applicability of deliverology to community economic development. Keywords: deliverology, performance measurement, results and delivery framework, community economic development, service delivery model
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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 | 0.133 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".