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Factors Influencing Funding Decisions by Elected Politicians at the State/Provincial Level: A Case Study of Public Libraries in Canada

2012· dissertation· en· W24721743 on OpenAlexaboutno aff
Cheryl Stenström

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
FundersEuropean Federation of Pharmaceutical Industries and AssociationsKræftens Bekæmpelse
KeywordsScarcityPoliticsContext (archaeology)Political scienceConsistency (knowledge bases)Public administrationState (computer science)Reciprocity (cultural anthropology)Public relationsBusinessEconomicsPsychologyGeographyLawSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine factors (internal and external) that influenced Canadian provincial (state) politicians when making funding decisions about public libraries. Using the case study methodology, Canadian provincial/state level funding for public libraries in the 2009-10 fiscal year was examined. After reviewing funding levels across the country, three jurisdictions were chosen for the case: British Columbia's budget revealed dramatically decreased funding, Alberta's budget showed dramatically increased funding, and Ontario's budget was unchanged from the previous year. The primary source of data for the case was a series of semi-structured interviews with elected officials and senior bureaucrats from the three jurisdictions. An examination of primary and secondary documents was also undertaken to help set the political and economic context as well as to provide triangulation for the case interviews. The data were analysed to determine whether Cialdini's theory of influence (2001) and specifically any of the six tactics of influence (i.e, and consistency, liking, social proof, scarcity and reciprocity) were instrumental in these budget processes. Findings show the principles of authority, and commitment and were relevant, and that were especially important to these decisions. When these decision makers were considering funding for public libraries, they most often used three distinct lenses: the consistency lens (what are my values? what would my party do?), the authority lens (is someone with hierarchical power telling me to do this? are the requests legitimate?), and most importantly, the liking lens (how much do I like and know about the requester?). These findings are consistent with Cialdini's theory, which suggests the quality of some relationships is one of six factors that can most influence a decision maker. The small number of prior research studies exploring the reasons for increases or decreases in public library funding allocation decisions have given little insight into the factors that motivate those politicians involved in the process and the variables that contribute to these decisions. No prior studies have examined the construct of influence in decision making about funding for Canadian public libraries at any level of government. Additionally, no prior studies have examined the construct of influence in decision making within the context of Canadian provincial politics. While many public libraries are facing difficult decisions in the face of uncertain funding futures, the ability of the sector to obtain favourable responses to requests for increases may require a less simplistic approach than previously thought. The ability to create meaningful connections with individuals in many communities and across all levels of government should be emphasised as a key factor in influencing funding decisions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0120.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.184
GPT teacher head0.401
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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