Academic search engines: constraints, bugs, and recommendations
Bibliographic record
Abstract
Academic search engines (i.e., digital libraries and indexers) play an increasingly important role in systematic reviews however these engines do not seem to effectively support such reviews, e.g., researchers confront usability issues with the engines when conducting their searches. To investigate whether the usability issues are bugs (i.e., faults in the search engines) or constraints, and to provide recommendations to search-engine providers and researchers on how to tackle these issues. Using snowball-sampling from tertiary studies, we identify a set of 621 secondary studies in software engineering. By physically re-attempting the searches for all of these 621 studies, we effectively conduct regression testing for 42 search engines. We identify 13 bugs for eight engines, and also identify other constraints. We provide recommendations for tackling these issues. There is still a considerable gap between the search-needs of researchers and the usability of academic search engines. It is not clear whether search-engine developers are aware of this gap. Also, the evaluation, by academics, of academic search engines has not kept pace with the development, by search-engine providers, of those search engines. Thus, the gap between evaluation and development makes it harder to properly understand the gap between the search-needs of researchers and search-features of the search engines.
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 | 0.217 | 0.747 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".