Does “Evaluating Journal Quality and the Association for Information Systems Senior Scholars Journal Basket…” Support the Basket with Bibliometric Measures?
Bibliographic record
Abstract
We re-examine “Evaluating Journal Quality and the Association for Information Systems Senior Scholars Journal Basket…” by Lowry et al. (2013). They sought to use bibliometric methods to validate the Basket as the eight top quality journals that are “strictly speaking, IS journals” (Lowry et al., 2013, pp. 995, 997). They examined 21 journals out of 140 journals considered as possible IS journals. We also expand the sample to 73 of the 140 journals. Our sample includes a wider range of approaches to IS, although all were suggested by IS scholars in a survey by Lowry and colleagues. We also use the same sample of 21 journals in Lowry et al. with the same methods of analysis so far as possible. With the narrow sample, we replicate Lowry et al. as closely as we can, whereas with the broader sample we employ a conceptual replication. This latter replication also employs alternative methods. For example, we consider citations (a quality measure) and centrality (a relevance measure in this context) as distinct, rather than merging them as in Lowry et al. High centrality scores from the sample of 73 journals do not necessarily indicate close connections with IS. Therefore, we determine which journals are of high quality and closely connected with the Basket and with their sample. These results support the broad purpose of Lowry et al., finding a wider set of high quality and relevant journals than just MISQ and ISR, and find a wider set of relevant, top quality journals.
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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 | 0.098 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".