A bibliometric analysis of race-related research in LIS
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
This special issue on race relations and racial inequity in Library and Information Science (LIS) is a response a recent wave of advocacy, activism, and protests. Its explicit purpose is to address the lack of research on race and inequity within our field. The purpose of this contribution to the issue is to substantiate that statement by performing a bibliometric analysis of the last 40 years of LIS scholarship to quantify the amount of attention given to race and racial inequality over that period. We find that despite an important increase in BIPOC-related research in LIS, the numbers remain quite low with approximately 2% of LIS publications containing terms related to racial inequality and BIPOC communities, and this research also tends to be less cited than the average LIS papers in the same area. We also find that this research is present in several areas of the field, although unevenly distributed across them. The trends presented in this paper may help when discussing sensitive issues regarding systematic discrimination, help create and sustain momentum towards change, and address the persistent lack of diverse perspectives and approaches across LIS scholarship and practice.
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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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.047 | 0.256 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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