Development and Implementation of a Bed Bug IPM Enrichment Curriculum, Part II
Bibliographic record
Abstract
As concerns about bed bug sightings began to increase and schools began to report bed bug introductions, an expert group convened by the Environmental Protection Agency recommended development of curricula to teach young students about bed bug biology and management. The Bed Bugs and Book Bags curriculum is our response to this recommendation. This third- through fifth-grade curriculum was developed using a six-step process and has been implemented by health educators, teachers, and the pest management industry within and outside of the United States. The curriculum consists of 10 lessons correlated with state health educational standards and is specifically designed to educate teachers and students about bed bugs. In pilot testing of various groups including teachers, students, custodial staff at rescue missions, and pest management professionals, teacher and student groups had the highest increase in knowledge gain after being introduced to the curriculum. The curriculum is available online at http://duval.ifas.ufl.edu/Bed_Bugs.shtml. Based on self-reporting, it has the potential to reach approximately 40,000 people across the United States, Canada, and Saudi Arabia.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".