The role of influence in city and public library partnerships: an exploratory study
Bibliographic record
Abstract
Purpose – The purpose of this article is to discuss whether interpersonal influence impacts the success of information technology support jointly managed by public libraries and their corresponding city departments. By exploring various management models of the information technology departments serving Canada's urban public libraries, the role of interpersonal influence in these partnerships is described. Design/methodology/approach – A two-part survey was administered to all Canadian urban libraries to explore which management models exist and to determine current issues. In-depth semi-structured interviews were conducted with exemplary sites. The survey data were used to rank dependence levels of public libraries on their corresponding cities. Using Cialdini's framework of influence, a thematic analysis was conducted on the interview data to note the presence or absence of each principle. Findings – Most Canadian urban public libraries rely on their corresponding cities for a small number of IT-related services; 25 percent have somewhat or highly integrated departmental partnerships. Interpersonal influence, particularly the principles of “authority” and “liking” are important factors in these partnerships. Research limitations/implications – This study is limited to Canadian urban public libraries and explores a single service. It builds on previous studies exploring the role of influence and public libraries, and indicates the utility of further research of city and public library partnerships. Practical implications – The findings may help inform the development of Library Service Level Agreements and other shared policy documents. Originality/value – This is the first study to explore shared management models and the role of influence at the municipal level in Canadian public libraries.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".