While Most Information Literacy Research Is Included in the Fields of Library Science and Education, a Considerable Amount Is Found in Medicine and Health
Bibliographic record
Abstract
A Review of: Aharony, N. (2010). Information literacy in the professional literature: An exploratory analysis. ASLIB Proceedings: New Information Perspectives, 62(3), 261-282. https://doi.org/10.1108/00012531011046907 Abstract Objective – To describe the published literature on information literacy from 1999-2009. Design – Statistical descriptive analysis and content analysis. Setting – N/A Subjects – 1,970 publications from the Web of Science database. Methods – The Web of Science database was searched using the term “information literacy” in the advanced search under “topic,” and was limited to articles published from 1999-2009. Next, information such as document type, subject areas, authors, source titles, publication years, languages, countries, keywords, and abstracts was collected from each document. A statistical descriptive analysis was conducted using the data. A content analysis was performed on the keywords and abstracts from a sampling of the results. Main Results – Information science/library science and education were the top subject areas of the identified articles, while the third largest subject area was “public, environmental and occupational health.” Nine out of ten journal titles focused on library science, however the journal title containing the second largest number of articles was Patient Education and Counseling. The content analysis revealed that the most common categories for keywords were “miscellaneous,” “health and medicine,” followed by “education.” Conclusion – The results indicated that information literacy research had been published mainly in journals associated with library science and education; however, a considerable amount of literature was published in health and medicine.
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 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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".