Surveying North American Academic Library Websites for Instructional Outreach and Delivery Reveals a Broad Range of Approaches Employed
Bibliographic record
Abstract
A Review of: Yang., S. Q., & Chou, M. (2014). Promoting and teaching information literacy on the Internet: Surveying the web sites of 264 academic libraries in North America. Journal of Web Librarianship, 8(1), 88-104. http://dx.doi.org/10.1080/19322909.2014.855586 Abstract Objective – To determine the extent to which academic libraries have used the Web to market and deliver information literacy both as a service and as a concept. Design – Survey of web content. Setting – Websites of North American academic libraries. Subjects – A random sample of 264 libraries selected from Peterson’s Four-Year Colleges. Methods – The investigators reviewed and analyzed content on academic library websites by recording the presence of various types of information. Presence was recorded for the term information literacy, tutorial content, guides and tests, and delivery of information literacy instruction. The frequencies of tutorials and guides were also reported. Main Results – Approximately 65% of the libraries used their website to promote instruction, while 30% did not mention information literacy or library instruction. A wide range of terminology was used to denote library instruction, but information literacy was not highly used. Approximately 5% of libraries had no public web presence. Research guides, tutorials, or both were provided by 64% of libraries. More than 300 tutorials in a variety of formats, including Adobe Flash videos, static web pages with little or no animations, webcasts, documents, and presentations were offered by 111 libraries. The tutorials addressed general research topics, databases, concepts and technical skills, among others. Conclusion – While the majority of academic libraries sampled have incorporated information literacy and library instruction into their web presence, it is unclear why nearly one third did not mention these activities. Further study is needed to benchmark how libraries are using the Web for instruction and outreach.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".