Assessing the Information Needs of Pollution Prevention Technical Assistance Providers in the Great Lakes Region
Bibliographic record
Abstract
The Great Lakes Regional Pollution Prevention Roundtable (GLRPPR) is a professional organization that facilitates networking among and develops and promotes information resources and training for sustainability professionals in the Great Lakes region of the U.S. and Canada. GLRPPR’s four focus areas are: Green Chemistry and Engineering; Technical Assistance; Behavior Change and Sustainability; and Sustainable Electronics. Because these focus areas are very broad, GLRPPR contracted with the University of Illinois’ Survey Research Laboratory (SRL) to conduct a regional information needs assessment in 2014. The assessment was designed to determine how pollution prevention (P2) technical assistance providers in the region currently locate and access information; how they stay up-to-date in the field; how they prefer to receive training; and what training and information gaps exist within these broad focus areas. GLRPPR staff worked with the SRL project manager to develop an assessment instrument, obtain Institutional Research Board approval, and encourage GLRPPR members to respond to the request to provide feedback. The full needs assessment report is available in IDEALS, the University of Illinois at Urbana-Champaign’s institutional repository, at http://hdl.handle.net/2142/73269.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".