Web link counts correlate with ISI impact factors: Evidence from two disciplines
Bibliographic record
Abstract
Abstract This paper reports on a study that compares counts of links to the home pages of academic journals with the citation based Impact Factor for two disciplines: library and information science; and law. A significant correlation between these two measures was found for both subjects covered with law showing a weaker relationship, although neither relationship was particularly strong. The weakness may be attributable to journal specific factors that encourage more linking such as: computing‐related content or particularly well developed Web sites; and wide distribution, perhaps including to a non‐academic audience that may link to the journal but would not be citing it. It is also possible that insularity in a discipline may inhibit link counts but not Impact Factors. This exercise can be seen as (a) a useful way to re‐examine the journal Impact Factors and (b) investigating a technique that is a potential source of additional information about the impact of a journal, particularly in terms of reaching out beyond a purely academic audience. The technical issues discussed show, however, the need for careful data collection and interpretation of results.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".