Bibliographic record
Abstract
In December 2021, the Canadian Association of Research Libraries (CARL) released the CARL Library Impact Framework (CLIF). While library impact has been a topic of discussion for many years, CLIF offers a new contribution to the dialogue on the demonstration of impact of research libraries. The concept of impact pathways was borrowed from the Federation for the Humanities and Social Sciences report entitled Approaches to Assessing Impacts in the Humanities and Social Sciences. To realize the impact pathways concept, CLIF has adapted a logic model framework. This approach provides users of CLIF with a way to represent a more complete arc of influence of research libraries systematically and visually. By design, CLIF encourages the use of assessment techniques and tools beyond the quantitative data collection and descriptive statistics often used by research libraries. This paper provides an overview of CLIF, its genesis, intent, structure, and possibilities for its application in research libraries.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.035 | 0.026 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".