Research Spotlights: Introducing a tool for showcasing the visibility of diverse populations in scholarly publications
Bibliographic record
Abstract
The ICPSR Bibliography of Data-related Literature is a freely-available and continually updated database of over 105,000 citations that link data to primary and secondary analyses by researchers across the world. Since 2020, the Bibliography’s Information Resource staff has been creating instructional resources called Research Spotlights, using the Bibliography as their source to synthesize the findings about one or several related topics. These Research Spotlights show how scholars are using data available from the Inter-university Consortium for Political and Social Research (ICPSR) in their analyses. This poster will showcase how we make use of Research Spotlights to highlight the diversity of populations represented across a variety of fields contained in the studies archived at ICPSR. In addition to shedding light on research publications using data connected to timely topics, the Research Spotlights written so far have been able to underscore how research findings are particularly relevant to specialized diverse communities, such as LGBTQ+ populations, women, or the elderly. The future goal for the Research Spotlights is to adopt better analytics to track their impact on data reuse. These short literature reviews increase awareness of the value of existing data to address new research questions. Data reuse is cost- and time-efficient, and it benefits users in many areas of social science including training and higher education. Librarians and other instructors can utilize Research Spotlights as data literacy tools to help students and emerging scholars find models for data reuse in the scholarly literature.
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 | 0.024 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.017 |
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".