GIScience Journals Ranking and Evaluation: An International Delphi Study
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 Researchers’ fame in most scientific fields is closely linked to their publishing capacity, both in terms of quantity and quality. In GIScience, as in other fields, this situation demands that the researcher evaluate and to be very familiar with the scientific journals in which they could publish. Some specialized journals (e.g. Journal of Citation Reports or JCR) are devoted to ranking these reviews according to various methods and criteria. Compared to other scientific communities, GIScience is relatively new and constantly evolving. Therefore, the journals of this field do not benefit from any real formal ranking yet. The objective of this paper is to present the process and results of a study aimed at addressing this gap. More specifically, the challenge is to elaborate an importance ranking of the scientific journals in the field of GIScience. To do so, both a qualitative (Delphi study carried out with 40 international experts) and a quantitative (JCR impact factor) approach has been implemented. This triangulation method leads to an early global ranking of the journals of this field.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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