A Scoping Review Protocol of Anti-Racism Programs and Practices in Higher Education: Implications for Developing Interventions to Advance Racial Equity
Bibliographic record
Abstract
This scoping review protocol aims to identify and map the characteristics of anti-racism programs and practices (occurring at the organizational level) in higher education settings throughout the United States and Canada. A systematic search will be conducted using nine electronic databases: Academic Search Ultimate (Ebscohost), ERIC (Ebscohost), APA PsycINFO (Ebscohost), Medline (OVID), Dissertations & Theses Global (ProQuest), Social Services Abstracts (ProQuest), Social Work Abstracts, Sociological Abstracts and Scopus (Elsevier). Date limits will be applied from 1950 to 2022. Reference lists of documents included in data charting will be searched. The scoping review will follow guidance from the most recent version of the JBI Manual for Evidence Synthesis and PRISMA reporting guidelines for scoping reviews (PRISMA-ScR). Full-text screening of preselected studies will be performed by two reviewers independently to select studies according to inclusion criteria. Covidence is used to upload search results, screen abstracts and full text study reports. Data will be extracted and findings and characteristics synthesized in a narrative summary. Additionally, frequency counts of concepts, populations, and characteristics will be presented.
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 | 0.268 | 0.223 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.027 |
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".