University–industry R&D linkage metrics: validity and applicability in world university rankings
Bibliographic record
Abstract
In September 2015 Thomson Reuters published its Ranking of Innovative Universities (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a critical view from a methodological perspective. We focus our attention on the various types of metrics available, whether or not data redundancies are addressed, and if metrics should be assembled into a single composite overall score or not. We address these issues in some detail by emphasizing one metric in particular: university–industry co-authored publications (UICs). We compare the RIU with three variants of our own University – Industry R&D Linkage Index , which we derived from the bibliometric analysis of 750 research universities worldwide. Our findings highlight conceptual and methodological problems with UIC-based data, as well as computational weaknesses such university ranking systems. Avoiding choices between size-dependent or independent metrics, and between single-metrics and multi-metrics systems, we recommend an alternative ‘scoreboard’ approach: (1) without weighing systems of metrics and composite scores; (2) computational procedures and information sources are made more transparent; (3) size-dependent metrics are kept separate from size-independent metrics; (4) UIC metrics are selected according to the type of proximity relationship between universities and industry.
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How this classification was reachedexpand
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.076 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.081 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".