Effective Practices and Strategies for Open Access Outreach: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION There are many compelling reasons to make research open access (OA), but raising the awareness of faculty and administrators about OA is a struggle. Now that more and more funders are introducing OA policies, it is increasingly important that researchers understand OA and how to comply with these policies. U.K. researchers and their institutions have operated within a complex OA policy environment for many years, and academic libraries have been at the forefront of providing services and outreach to support them. This article discusses the results of a qualitative study that investigated effective practices and strategies of OA outreach in the United Kingdom. METHODS Semistructured interviews were conducted with 14 individuals at seven universities in the United Kingdom in late 2015. Transcripts of these interviews were analyzed for dominant themes using an inductive method of coding. RESULTS Themes were collected under the major headings of “The Message”; “Key Contacts and Relationships”; “Qualities of the OA Practitioner”; and “Advocacy versus Compliance.” DISCUSSION Results indicate that messages about OA need to be clear, concise, and jargon free. They need to be delivered repeatedly and creatively adapted to specific audiences. Identifying and building relationships with influencers and informers is key to the uptake of the message, and OA practitioners must have deep expertise to be credible as the messengers. CONCLUSION This timely research has immediate relevance to North American libraries as they contend with pressures to ramp up their own OA outreach and support services to assist researchers in complying with new federal funding policies.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".