Influence and increased funding in Canadian public libraries : the case of Alberta in fiscal year 2009–10
Bibliographic record
Abstract
This exploratory case study examined the role of social influence in the decision-making process to increase public library funding in the Canadian province of Alberta in the 2009–10 fiscal year. Using Robert Cialdini’s theory of factors of influence (i.e., commitment and consistency, authority, liking, social proof, scarcity, and reciprocity) as a framework for analysis, findings show that consistency and commitment and authority were relevant and that liking was also important. These findings are consistent with Cialdini’s theory, which suggests that the quality of relationships is one factor that can most strongly influence a decision maker. This study gives insight into the factors motivating those involved in public library funding allocation decisions. No prior studies have examined the construct of influence in decision making about funding for public libraries at any level of government.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".