Eleven Ways to Measure the Immeasurable and Count the Incalculable
Bibliographic record
Abstract
In 2017 Ocean Networks Canada (ONC), a research infrastructure operator, sought to redefine its core reporting metrics. We asked, “which metrics should we hold as key, essential metrics to drive our organizational priorities and decision making?” This question helped us define a collection of eleven sets of yardsticks, some inward-looking, others squarely focused on societal outcomes. Here, we introduce the individual metrics adopted, insights they are helping us glean and some of their inherent challenges. ONC’s core funding agency, the Canada Foundation for Innovation (CFI), continues to emphasize scientific output as a primary criterion. We measure this by counting peer-reviewed presentations and publications resulting from use the facility and ONC’s data archives. But this seemingly clear-cut metric has been a thorny one to define, track and grow. Training and support for post-secondary students is another core reporting metric, however this measurement is also fraught with ambiguities. Some of the easier metrics to track are those specifically related to facility operations, such as reliability and user satisfaction. But we were perplexed by the question of how to measure “optimal use” of the facility, as mandated by CFI. Optimal use is hard to define for an underwater infrastructure design like ONC’s, which can be flexibly extended with no hard limits on hardware capacity, archive volume or data access. When it comes to societal benefit, our approach has been twofold. One set of metrics examines technology transfer, grants and contracts. Another set focuses on our engagements and active collaborations with governmental, indigenous and non-governmental organizations. However, some outcomes remain challenging to measure. While it is straightforward to count up our external interactions and collaborations, how can we quantify their current and future societal impact? These and related questions will be explored.
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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 | 0.123 | 0.292 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.029 | 0.043 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".