Institutional repository statistics: reliable, consistent approaches for Canada
Bibliographic record
Abstract
Introduction: “Usage metrics are an effective way for libraries to demonstrate the value of their institutional repositories, however, existing tools are not always reliable and can either undercount or overcount file downloads. As well, although statistics can sometimes be accessed through the various repository interfaces, without an agreed standard it is impossible to reliably assess and compare usage data across different IRs in any meaningful way.”[1] The Task Group for Standards for IR Usage Data has undertaken an information-gathering exercise to better understand both the existing practices of Canadian repositories, as well as the emerging tools and processes available for repositories to track and monitor usage more effectively. This exercise directly links to the broader goals of the Open Repositories Working Group, which are to “strengthen and add value to the network of Canadian open access repositories by collaborating more closely and adopting a broader range of services.”[2] Our recommended course of action is for all Canadian IRs to collectively adopt OpenAIREStatistics. This path aligns with the following recommendations which our group also advances: Recommendations: We suggest the following Mandatory (M) and Optional (O) recommendations: R1(M):All Canadian IRs should adopt the COUNTER Code of Practice. R2(M): All Canadian IRs should select a service that allows for interoperability with other web services via a fully open, or accessible, permissions-based API. R3(M): All Canadian IRs should usea statistics service that practices transparent communication and maintains a governance strategy. In addition, we strongly urge for the future that Canadian IRs consider the following advice. R4(O): Make further investments into understanding and utilizing the common log format (CLF). R5(O): Conduct research into the privacy implications of collecting use statistics via third party services with commercial interests and consider available alternatives. R6(O): Practice a healthy skepticism towards tools and solutions that promise “increased” usage statistics, and instead advocate for responsible collection assessment based on multiple aspects of use.
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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 | 0.110 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.034 | 0.062 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.042 | 0.021 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".