Bibliographic record
Abstract
Purpose – Scholars rely on electronic databases to conduct searches and locate relevant citations. The purpose of this paper is to compare the retrieval results on the same topic (multiple sclerosis and liberation therapy) of two commonly used databases for searching print news media: ProQuest’s Canadian Newsstand Major Dailies and Dow Jones’ Factiva. Design/methodology/approach – A case study comparing two electronic searchable databases using the same keywords, date range, and newspaper-specific search parameters across three Canadian university institutions. Findings – Considerable differences were found between institutional searches using Factiva. Factiva allows all individual users the capacity to establish systems-wide “administrator” privileges, thereby controlling the output for subsequent users if these preferences are not changed. The capacity for individual users to tailor searches within Canadian Newsstand Major Dailies was more in line with standard protocols for institutions paying for single user accounts with access to multiple sessions within that same institution: any user-specific searching/retrieval preferences are individually contained within a search and do not influence the searches of a different user. Research limitations/implications – What began as a comparative analysis of two commonly used databases for searching print news media turned into an examination of larger systemic problems. The findings call into question several factors: the integrity of a researcher-generated data set; the quality of results published in peer-reviewed journals based on researcher-generated data sets derived from established e-resource databases; the reliability of the same e-resource database across multiple institutions; and the quality of e-resource databases for scholarly research when developed to serve primarily non-academic clients. Originality/value – No comparison of this kind for these particular e-resource databases has been documented in the literature. In fact, the scholarly publications that address questions of functionality and reliability of either Factiva or Proquest have not brought this issue into the discussion. Therefore, this study furthers academic discourse on the nature and reliability of database use at any academic institution and illustrates that researchers, in a variety of academic fields, cannot depend on the reliability of their search results without thoroughly consulting the various settings of their database.
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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".