Evaluating Equity and Inclusion in Cultural Heritage Grantmaking: Report and Supplementary Materials
Bibliographic record
Abstract
This report summarizes a yearlong program assessment of "Amplifying Unheard Voices," a major revision of CLIR's Digitizing Hidden Collections grant program. The revision sought to expand the reach and appeal of the program to a broader range of institutions, including independent and community organizations, and to emphasize the digitization of historical materials that tell the stories of groups underrepresented in the digital historical record. Significant changes were made to the application structure, new applicant support resources were created, eligibility was expanded to Canada, and new thematic emphases and program values were added. The assessment was based on a series of qualitative data-gathering activities that included stakeholder groups and staff. Through surveys and interviews of applicants, inquirers, proposal reviewers, and staff, the authors provide a holistic view of the program, offer a series of recommendations, and identify areas for further attention. This zenodo repository contains the full report (pdf) as well as supplemental data files that include anonymized survey responses (xlsx), survey instruments (docx), and interview protocols (docx). All materials can also be found at https://www.clir.org/pubs/reports/dhc-auv-assessment/.
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 | 0.095 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".