Increasing libraries' content findability on the web with search engine optimization
Bibliographic record
Abstract
Purpose The aim of this paper is to examine the phenomenon of search engine optimization (SEO) as a mechanism for improving libraries' digital content findability on the web. Design/methodology/approach The study applies web analytical tools, such as Alexa.com, in the collection of data about Canadian libraries' visibility performance in the ranking of search engine results. Concepts from the Integrated IS&R Research Framework are applied to analyze SEO as an element within the Framework. Findings The results show that certain websites' characteristics do have an effect on how well libraries' websites are ranked by search engines. Notably, the reputation of a library's website and the number of its search engine indexed webpages increase its ranking on SERPs as well as the findability of its digital content. Originality/value Most of the existing works on SEO have been confined to popular literature, outside of scholarly academic research in library and information science. Only few studies with a focus on libraries' application of SEO exist. No known study has applied an empirical approach to the examination of relevant libraries' website characteristics to determine their visibility performance on search engine result pages (SERPs). This study identified several website characteristics that can be optimized for higher SERP rankings. It also analyzed the impact of external links, as well as that of the number of indexed webpages by search engines on higher SERP rankings.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".