How Information in Grey Literature Informs Policy and Decision Making: The Need to Understand the Processes
Bibliographic record
Abstract
Our bibliometric research will examine a select set of documents in the Homeland Defense and Security Information Analysis Center’s (HDIAC) collection. The focus will be on documents in the “Cultural Studies” focus area. There are over 210,000 documents in the HDIAC collection, and much of it is grey literature. Staff use a template that includes bibliographic information, keywords, task areas, and other descriptive information to catalog items for inclusion in HDIAC’s database. In staff discussions, it was discovered that some focus areas intersect with other focus areas. For example, Cultural Studies overlaps with the Medical, Alternative Energy, Critical Infrastructure Protection, and Homeland Defense & Security focus areas. The purpose of this research is to examine the intersects that Cultural Studies has with the other HDIAC focus areas by examining the keywords and task area terms that include the term “Cultural Studies.” This process will assist in clarifying the subject key words used to identify cultural studies and facilitate the tagging, acquisition, and discovery processes. It will also give staff a model they can use to gain a deeper understanding of how culture intersects with other disciplines. A bibliometric study will be conducted to evaluate the HDIAC database, Scientific and Technical Analysis and Research Tool (START), and quantify the Activity Index (AI) of the HDIAC 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 | 0.387 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.109 | 0.156 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.096 | 0.100 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".