Identifying and Mapping Study of the Information Professional in Library with Scientometric Analysis
Bibliographic record
Abstract
The development of information in the digital era forces librarians to change their roles to become information professionals who have modern skills to face challenges in the digital environment. This study aimed to determine the extent of the studies conducted on information professionals in libraries and to find out the themes and terms that were often used, the trend of topics each year, and the social networks of the authors. The method used was Scientometric analysis using a single search in the Lens.org database. Articles were searched using the terms “information professional” AND “library” in the title. The data obtained were 1523 publications from 1950 to 2020. The results of this study showed that in 2011 and 2014 the largest number of publications were 76 and 84 articles, respectively. In addition, the average growth rate related to publications among information professionals was quite high at 29% during the analyzed period. The study themes were divided into 4 major theme groups and the basic theme was the most frequently used. Then the term that most often appears was "information" with 1110 repetitions. There were also technical terms such as digital, application, and internet which indicate that the study of information professionals had adapted to systems in the digital era. Following the trend of topics in the third quarter (2013-2020), it showed more about the LIS and skills.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.086 | 0.092 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".