The value of data and other non-traditional scholarly outputs in academic review, promotion, and tenure in Canada and the United States
Bibliographic record
Abstract
Academics are regularly involved in a wide range of activities spanning research, teaching and service, and the breadth of necessary outputs for review, promotion, and tenure (RPT) in each category only continues to grow. How do faculty manage their academic careers in the face of such growing sets of demands? Although we know that discussions of research assessment across the academy are increasingly recognizing the need to value the creation of outputs beyond research published in peer-reviewed journals, it is not clear whether these discussions have made their way into formal assessment structures. By analyzing the extent to which non-traditional outputs, including data and software, are mentioned in the RPT documents of a representative set of 129 universities from the United States and Canada, this chapter offers empirical evidence from across many disciplines of which types of faculty work are recognized in the RPT processes, and which are not. We confirm that traditional outputs such as peer-reviewed journal articles, book chapters and monographs are mentioned almost universally, whereas data-related items such as datasets and databases are mentioned only by a fraction of institutions. We find that research-intensive institutions acknowledge more types of research outputs in general, whereas institutions that focus more on undergraduate and master's degree programs tend to mention fewer forms of scholarship in their RPT guidelines. Within research-intensive institutions, units from the life sciences present a greater range of outputs in the guidelines offered to faculty, including the 15% that explicitly mention data-related outputs. In contrast, none of the academic units in mathematics and physical and social sciences in our sample recognize data-related outputs, and generally recognize fewer forms. Overall, we conclude that many current structures for faculty assessment do not explicitly recognize the increasing complexity and demands of faculty work.
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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 | 0.033 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.039 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".