A Hierarchy of Metadata Elements for Business Intelligence Information Resource Retrieval
Bibliographic record
Abstract
Effectively managing information resources is an important activity contributing to the competitive advantage of modern organizations. Organizational knowledge workers must be able to search for pertinent information quickly and effectively. This research identifies the relative usefulness of the metadata elements associated with the Dublin Core metadata standard for the effective retrieval of three different information resources – structured business intelligence reports, structured spreadsheet reports, and unstructured reports in formats such as Word and PowerPoint. A survey of knowledge workers was conducted to determine the relative usefulness of the metadata elements for each of the three information resources and to develop a framework outlining where metadata tag requirements differ between such resources. Overall, the study and resulting framework emphasize the need for system developers and database management personnel to be cognizant of the type of information resources being used, and ensure that search metadata elements that are appropriate for these specific resources are in place.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".