Web authoring tools and meta tagging of page descriptions and keywords
Bibliographic record
Abstract
Purpose To determine the effect of web page editing tools on inclusion and page specificity of meta tagged descriptions and keywords. Design/methodology/approach Using customized software with Yahoo!'s random page service, data from 2,048 URLs were logged. Generator identification was cross‐tabulated with presence and length of both descriptions and keywords. A second analysis on pages on geocities.com was performed using URLs from Altavista. Local links from a sample of the Yahoo! set were followed and linked‐to pages were examined for presence of description or keywords and whether these differed from those on the linking pages. Findings The Yahoo! set showed generally no significant difference in inclusion of descriptions and keywords between generator‐identifying and other pages. The geocities.com set did show a significant difference for both keywords and descriptions. Exact repetition of descriptions or keywords between pages on the same site did not generally correlate significantly with identified generators. Research limitations/implications Various other tools may have been used to create both generator‐identifying and other pages. A third factor, author level, is probably influencing both choice of authoring tool and decision to include description and keywords. How well keywords and descriptions actually fit the pages was not examined. Further research might examine other differences between the two sets. Practical implications Assistance with keywords and descriptions varies widely among web site editing packages, but is not a major selection criterion. Originality/value The hypothetical relationships had not previously been tested.
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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 | 0.131 | 0.683 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".