Carl-Coar Joint Webinar On Ir Usage Statistics
Bibliographic record
Abstract
Institutional repositories (IRs), by virtue of their ability to give increased visibility to the institution’s scholarly outputs, are valued for their vast amount of open scholarly content. Libraries wishing to demonstrate use (and value) frequently report the number of file downloads sustained by their IR. However, commonly used analytics tools are unsuited for this purpose and produce results that dramatically under-count or over-count 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. The first part of this webinar will explain the reasons for the inaccuracies in most IR download counts and will introduce a new web service called Repository Analytics and Metrics Portal (RAMP), which provides much more accurate counts of file downloads to IR managers, with almost no installation or training requirements. Aggregated data collected with RAMP also creates the potential for interesting new streams of research about IR. RAMP was developed with funding from the Institute of Museum and Library Services. The second half of this webinar will focus on another approach at standardizing institutional research data download statistics: IRUS-UK, a national aggregation service, which contains details of all content downloaded from participating IRs in the UK. By collecting raw usage data and processing them into item-level usage statistics, following rules specified by COUNTER, IRUS-UK provides comparable and authoritative standards-based data and also acts as an intermediary between UK repositories and other agencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.142 | 0.163 |
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".