Measuring the Economic Impact of Water Quality Initiatives: A Case Study of the Fund for Lake Michigan
Bibliographic record
Abstract
The Fund for Lake Michigan is an organization that invests in nonprofit and government organizations which conduct projects intended to clean up the environment. The main goal of the Fund for Lake Michigan is to improve the quality of Lake Michigan and the life of its communities. This paper conducts an analysis of the total economic impact of all Fund for Lake Michigan-funded projects between 2011 and 2013. The methodology used is IMPLAN, an input-output method of analysis that estimates to what extent different spending categories affect the local economy in terms of direct, indirect, and induced spending. Both primary impacts (those impacts that are directly caused by the Fund for Lake Michigan) and secondary impacts (those impacts that are indirectly caused by the Fund for Lake Michigan) were considered. The primary finding of this study is that the Fund for Lake Michigan has had a very positive, demonstrable economic impact in the southeastern region of Wisconsin including, but not limited to, creation of over 480 full-time equivalent jobs and increasing property values by over $45.5 million. Our findings also suggest that, if funded in the same manner, the Fund for Lake Michigan should continue to have a similar level of economic impact for the foreseeable future.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".