Bibliographic record
Abstract
Coverage of the 2014 Annual Conference of the Engagement Scholarship Consortium, featuring
 
 The keynote address from Dr. Rajesh Tandon
 Poster Session Award Winners
 
 First Place—Integrating High-Impact Scholarship into a General Education Class. By Careen Yarnal and Hsin-Yu Chen (The Pennsylvania State University)
 Second Place—The Impact of Homelessness and Incarceration on the Health of Women. By Louanne Keenan and Rabia Ahmed (University of Alberta)
 Third Place—Does Service-Learning Make Graduates (Feel) More Employable? By Paul H. Matthews and Jeffrey H. Dorfman (University of Georgia)
 Honorable Mention with Distinction
 
 Illustrating the Impacts: Global Community Engaged Design. By Rebekah Radtke and Travis Hicks (University of North Carolina-Greensboro).
 Oklahoma Cooperative Extension Service: Building an Interculturally Competent Community. By Maria G. Fabregas Janeiro and Jorge Atiles (Oklahoma State University).
 Characteristics of Effective Practice by Faculty in Service-Learning Courses. By Paul H. Matthews (University of Georgia) and Andrew J. Pearl (University of North Georgia).
 
 
 
 
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.304 | 0.164 |
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".