Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore and describe the decision-making practices of public library managers in the context of interpersonal influence and evidence-based information sources, and to investigate the relationship between models of evidence-based practice and interpersonal influence in the decision-making process of public library managers. Design/methodology/approach – Data were collected through short audio blog posts participants made about their everyday decisions and coded considering the facets of three existing evidence-based library and information practice (EBLIP) models as well as the facets of interpersonal influence. Findings – The findings show that public library CEOs decision-making behaviours reflect the use of a variety of practices from analytical to intuitive as is expected of managers in any sector; however, a stronger reliance on gathering objective information may be present than in other sectors. Seeking multiple sources of information and a tendency towards rationalism may indicate a more sophisticated approach to decision making, but be less indicative of the practices employed more broadly. A possible outcome of these tendencies may result in discordance with external partners and collaborators. Practical implications – The findings from this study may inform the work of associations, library and information science (LIS) educators, and library managers in developing strategic directions and instructional strategies within their organisations. It is also the first study to jointly examine models of interpersonal influence and evidence-based decision-making practices in any field. Originality/value – While the study of the decision-making practices of various groups is growing, little previous research has been conducted with public library managers, and none has been undertaken in Canada.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".