SPEC Kit 361: Outreach and Engagement
Bibliographic record
Abstract
���������������������������������������������������������������������������������������������� Draft: Event Planning for Therapy Dogs ������������������������������������������������������������������ Over the past 10 years, have had a great variety of regular programming covering trending diversity and inclusion topics, including #metoo, cultural communication, etc. Refugees; Human Library event; Parents Cafe (families of students); Research/lab group presentations; graduate student social event (speed dating the research experts) as introduction to research services Retirees of the university (Oak Hammock); study-abroad participants; offsite cohorts such as CityLab in Orlando and Sarasota (architecture graduate program)The university and the library aim to be inclusive and provide outreach programming that is universally accessible.This year we have a specific program targeted toward HIV/AIDS activists, which includes a great number of people living with HIV.This is an inter-sectional group that includes many people of color, LGBTQIA people, and low-income people.Transfer students TRIO Programs, transfer students, Women in Science and Engineering Undocumented students, transfer students, and Southeast Asian communityWe do not specifically target outreach and engagement with each of the above groups, but we do incorporate representatives of these groups into our advisory councils, and we consider diversity and inclusion engagement generally within many of our library programs and services.4. Which of the following outreach activities did your library engage in during the last calendar year?Check all that apply.N=57In-person tours 56 98%
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.052 |
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".