Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We are delighted to present this special issue of the Journal of Data and Information Quality on web data quality. This issue includes four innovative research articles covering the areas of web data profiling, web data quality assessment, and web data cleansing.Over the last few years, the volume and variety of data that is available on the Web has risen sharply. In addition to traditional data sources and formats such as CSV files, HTML tables, and deep web query interfaces, new techniques such as microdata, RDFa, microformats, and linked data have found wide adoption. In parallel, techniques for extracting structured data from web text and emistructured web content have matured resulting in the creation of large-scale knowledge bases such as NELL, YAGO, DBpedia, and the Knowledge Vault. Independent of the specific data source or format or information extraction methodology, data quality challenges persist in the context of the web. Applications are confronted with heterogeneous data from a large number of independent data sources while metadata is sparse and of mixed quality. Before one can utilize the data, a potential user must first overcome the challenges of handling a wide range of quality issues in the available data and metadata.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.026 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it