Web data as academic and business quality estimates: A comparison of three data sources
Bibliographic record
Abstract
Earlier studies found that web hyperlink data contain various types of information, ranging from academic to political, that can be used to analyze a variety of social phenomena. Specifically, the numbers of inlinks to academic websites are associated with academic performance, while the counts of inlinks to company websites correlate with business variables. However, the scarcity of sources from which to collect inlink data in recent years has required us to seek new data sources. The recent demise of the inlink search function of Y ahoo! made this need more pressing. Different alternative variables or data sources have been proposed. This study compared three types of web data to determine which are better as academic and business quality estimates, and what are the relationships among the three data sources. The study found that Alexa inlink and G oogle URL citation data can replace Y ahoo! inlink data and that the former is better than the latter. Alexa is even better than Y ahoo!, which has been the main data source in recent years. The unique nature of A lexa data could explain its relative advantages over other data sources.
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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 | 0.032 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.031 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".