Academic Performance Indicators for Departments of Geography in the United States and Canada
Bibliographic record
Abstract
A common problem faced by geography departments, particularly during times of fiscal compression and mounting pressure for accountability, is how to compare themselves and their faculty with others. The recent revolution in bibliometrics provides a growing volume of data that can be used in benchmarking exercises. In this article, we assess the production and citation of journal articles and books by tenure-track and tenured faculty in selected U.S. and Canadian geography departments (n = 17) according to a set of readily derived and transparent performance indicators derived from publicly available data. Scopus was used to assess article production and citation; Google Scholar was used for book citation. Results point to significant heterogeneity in department characteristics, productivity, and citation of published work. The number of publications, citations, and h-Index scores among scholars in the sample (n = 369) is related strongly to academic age and subfield of enquiry (i.e., physical or human geography) but not—despite apparently marked differences in output and citations—to gender.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".