Being where the people are: the challenges and benefits of search engine visibility for public libraries
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the challenges and benefits presented by search engine visibility for public libraries. This paper outlines the preliminary results of a pilot study investigating search engine visibility in two Canadian public libraries, and discusses practical approaches to search engine visibility. Design/methodology/approach – The study consists of semi-structured interviews with librarians from two multi-branch Canadian public library systems, combined with quantitative data provided by each library, as well as data obtained through site-specific searches in Google and Bing. Possible barriers to visibility are identified through thematic analysis of the interviews. Practical approaches are identified by the author based on a literature review. Findings – The initial findings of this pilot study identify a complex combination of barriers to visibility on search engines, in the form of attitudes, policies, organizational structures and technological difficulties. Research limitations/implications – This paper describes a small, preliminary pilot study. More research is needed before any firm conclusions can be reached. Practical implications – A review of the literature shows the increasing importance of search engine visibility for public libraries. This paper outlines practical approaches which can be undertaken immediately by libraries, as well as delving into the underlying issues which may be affecting libraries’ progress on the issue. Originality/value – There has been little original research investigating the reasons behind libraries’ lack of visibility in search engine results pages. This paper provides insight into a previously unexplored area by exploring public libraries’ relationships with search engines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".