SPEC Kit 361: Outreach and Engagement
Bibliographic record
Abstract
Library outreach is experiencing a renaissance. Librarians have been reaching out to their communities and developing programming for decades, but libraries are increasingly being asked to demonstrate their value to the communities that they serve. In response, outreach positions are becoming more commonplace and communities of practice are emerging around measuring the impact of library outreach activities. This SPEC Kit was born out of the authors’ struggles and successes in providing academic library outreach services at their local institutions. The survey questions were designed to gather information from ARL institutions to create a picture of library outreach that spans across institutions; a professional baseline. Questions of organizational priorities, vision, goals, resource allocation, staffing models, and assessment come together to paint the picture of how libraries are approaching outreach programs. The survey was sent to the 125 ARL member institutions in July 2018, with 57 (46%) responding by the August 6 deadline. The data gathered suggests that systematic outreach programs are still very much in their infancy and highly dependent on local organizational culture. This SPEC Kit highlights the areas where libraries share approaches to outreach programs while also shining a spotlight on issues that warrant continued research and attention by outreach librarians and library administrators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".