Harnessing the Power of Education Research Databases With the Pearl-Harvesting Methodological Framework for Information Retrieval
Bibliographic record
Abstract
Digital technologies enable the storage of vast amounts of information, accessible with remarkable ease. However, along with this facility comes the challenge to find pertinent information from the volumes of nonrelevant information. The present article describes the pearl-harvesting methodological framework for information retrieval. Pearl harvesting relies on the sampling of articles from a body of literature to extract the relevant search keywords. The general steps for using this method were applied to finding the essential list of search keywords for the topic of developmental disabilities. The success with the present investigation suggests that pearl harvesting might be used as a framework to develop keyword search lists in other areas, thereby providing a general methodology to help manage comprehensive literature reviews.
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 | 0.198 | 0.416 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.076 | 0.071 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.032 | 0.028 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".