Modifying the journal impact factor by fractional citation weighting: The audience factor
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
Abstract A new approach to the field normalization of the classical journal impact factor is introduced. This approach, called the audience factor , takes into consideration the citing propensity of journals for a given cited journal, specifically, the mean number of references of each citing journal, and fractionally weights the citations from those citing journals. Hence, the audience factor is a variant of a fractional citation‐counting scheme, but computed on the citing journal rather than the citing article or disciplinary level, and, in contrast to other cited‐side normalization strategies, is focused on the behavior of the citing entities. A comparison with standard journal impact factors from Thomson Reuters shows a more diverse representation of fields within various quintiles of impact, significant movement in rankings for a number of individual journals, but nevertheless a high overall correlation with standard impact factors.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.067 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.000 |
| 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