Evaluating the Trend of Using New Technologies to Attract Audience in Public Libraries in Iran
Bibliographic record
Abstract
Background and Objective: This study examined the use of modern technology to absorb audience in public libraries under the Public Libraries Foundation.Methodology: This is a survey- kind of descriptive study. The statistical population are authorities of public libraries with standard and central level. A questionnaire was used to collect the data. Reliability of the questionnaire was calculated using Cronbach's alpha of 0. 871. Software SPSS19 was used to analyze data analysis.Findings: The results showed that the use of new technologies in public libraries is lower than the average level. The most use of these devices is shown in Tabriz Central Library at 58 percent. Also, there is no significant difference between respondents' opinions in terms of demographic variables, level and degree of education.The possible results and applications: The results of this research are useful for decision making and effective use of new technologies to attract and expand audiences in public libraries.Originality / value: This study is among the first research that examines new technologies in public libraries to attract audience. Earlier in marketing literature and web technologies, several studies have been conducted for public libraries, however, in this study the application of new technologies in libraries is used to increase to attract audience.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".