Bibliographic record
Abstract
Today, diversity is defined beyond racial and ethnic groups and includes gender, sexual orientation, ability, language, religious belief, national origin, age, and ideas. The increase of published literature about cultural competencies, microaggressions, and assessment of diversity issues, as well as the inclusion of social justice movements in libraries, suggests diversity-related activities have increased and evolved over the last seven years. Over this time span, several libraries have obtained funding to support strategies to increase the number of minority librarians on their staff and support their advancement within the organization. There also appears to be an increase in the number of diversity or multicultural groups at the local, state, and national levels. However, these changes have not been consistently documented. Therefore, it is important to re-examine this topic to evaluate the impact of evolving endeavors, to see if more ARL libraries are involved, to see how diversity plans have changed over the years, and to document the current practices of research libraries. The main purpose of this survey was to identify diversity trends and changes in managing diversity issues in ARL libraries through exploring the components of diversity plans and initiatives since 2010, acknowledge library efforts since the 1990s, provide evidence of best practices and future trends, and identify current strategies that increase the number of minority librarians in research libraries and the types of programs that foster a diverse workplace and climate. The survey was conducted between May 1 and June 5, 2017. Sixty-eight of the 124 ARL member institutions responded to the survey for a 55% response rate. Interestingly, only 22 of the respondents to the 2010 SPEC survey participated in this survey, but this provides an opportunity to explore the diversity and inclusion efforts of a new set of institutions in addition to seeing what changes those 22 institutions have made since 2010. The SPEC Survey on Diversity and Inclusion was designed by Toni Anaya, Instruction Coordinator, and Charlene Maxey-Harris, Research and Instructional Services Chair, at the University of Nebraska-Lincoln. These results are based on responses from 68 of the 124 ARL member libraries (55%) by the deadline of June 12, 2017. The survey’s introductory text and questions are reproduced below, followed by the response data and selected comments from the respondents. The purpose of this survey is to explore the components of diversity plans created since 2010, identify current recruitment and retention strategies that aim to increase the number of minority librarians in research libraries, identify staff development programs that foster an inclusive workplace and climate, identify how diversity programs have changed, and gather information on how libraries assess these efforts.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.368 | 0.149 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".