Further Research is Required to Determine Which Database Products Best Support Research in Public Administration
Bibliographic record
Abstract
A review of: Tucker, James, Corey. “Database Support for Research in Public Administration.” Behavioral & Social Sciences Librarian 24.1 (2005): 47-60. Objective – To examine the extent to which six commercial database products support student and faculty research in the area of public administration. Design – Bibliometric study. Setting – Academic library in the United States. Subjects – Six commercial business-related database products were examined: Proquest’s ABI/INFORM Global edition (ABI), EBSCO’s Business Source Premier (BSP), Gale’s General BusinessFile ASAP (GBF), EBSCO’s Academic Search Premier (ASP), EBSCO’s Expanded Academic Index (EAI) and Proquest’s International Academic Research Library (ARL). Three of the databases (ABI, BSP, GBF) were chosen because they address the management, human resource, and financing elements of public administration. The other three (ASP, EAI, ARL) were included because of their multidisciplinary coverage. Methods – A list of journal titles covering public administration was assembled from the Institute of Scientific Information’s Social Sciences Citation Index and previously published lists of recommended journals in the field. The author then compared the compiled list of journal titles against the journal titles indexed by the six database products. He further analyzed the results by level of journal coverage (abstract only, full-text, and full-text with embargo) and subject area based on categories described in Ulrich’s Periodicals Directory. Main Results – The study found that three of the six database products --EAI, BSP, and ARL -- provide indexing for the greatest number of public administration journals contained in the compiled list. EIA and ARL cover the greatest number of those that are full-text journals, while BSP and ASP cover the greatest number of those full-text journals limited by publisher embargoes. Conclusion – The author concludes that of the six databases examined, EAI, BSP, and ARL are the best for public administration research, based on their strength in the subject areas of public administration and public finance. The author also recommends that librarians in the field of public administration “carefully evaluate each database to see which one best fits the needs of the library and patrons” (56).
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 | 0.107 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.050 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.031 | 0.039 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".