Study in Grey and White: Measuring the Impact of the 8Rs Canadian Library Human Resources Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective – To use the 8Rs Canadian Library Human Resources Study (the 8Rs Study) as a test case to develop a model for assessing research impact in LIS. 
 
 Methods – Three different methods of citation analysis which take into account the changing environment of scholarly communications. These include a ‚manual‛ method of locating citations to the 8Rs Study through a major LIS database, an enhanced-citation tool Google Scholar, and a general Google search to locate Study references in non-scholarly documents 
 
 Results – The majority of references (82%) were found using Google or Google Scholar; the remainder were located via LISA. Each method had strengths and limitations.
 
 Conclusion - In-depth citation analysis provides a promising method of understanding the reach of published research. This investigation’s findings suggest the need for improvements in LIS citation tools, as well as digital archiving practices to improve the accessibility of references for measuring research impact. The findings also suggest the merit of researchers and practitioners defining levels of research impact, which will assist researchers in the dissemination of their work.
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.
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 | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.594 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it