Analyzing the Economic Impacts of Events within Prince George’s County
Bibliographic record
Abstract
This collaborative project between PALS students, and representatives from M-NCPPC, Department of Parks and Recreation (DPR), Prince George’s County began as a data analysis effort. We were to analyze existing data from M-NCPPC, Department of Parks and Recreation for insights into how hosted events and rental facilities impact the economics of Prince George’s County as a whole. For example, one task would consist of analyzing the amount of money spent within Prince George’s County by out-of-County tourists during their attendance at a M-NCPPC, Department of Parks and Recreation service or event. Due to the COVID-19 outbreak, many changes have been instituted by state and local governments. Restrictions on group activities and indoor facility use have impacted DPR operations and limited the scope of the services that they can provide during the outbreak. We can expect that the scope will return to its usual size as restrictions are lifted, but in the meantime, there is a new opportunity to capture data about Parks and Recreation users in Prince George’s County. We hope that this data capture will ultimately help the Department of Parks and Recreation in formulating new insights as COVID-19 continues to impact organizations and people. This shift of scope altered our data analysis project into one of data capture. The task is to create a data collection method that will help capture economic losses endured by Prince George’s County due to the service cancellations caused by the COVID-19 outbreak. This method will also capture user interest in online-formatted services hosted by M-NCPPC, Department of Parks and Recreation. We also aim to capture current user behavior of remaining assets (parks and trails). The chosen data collection will be two surveys.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".