Web authoring tools and meta tagging of page descriptions and keywords
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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