Study in Grey and White: Measuring the Impact of the 8Rs Canadian Library Human Resources Study
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.
How this classification was reachedexpand
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 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 | 0.052 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".