Bibliographic record
Abstract
The lack of diversity in the library and information science (LIS) field is a historical problem in a profession that strives to provide access to information for all. Many librarians of Latin American heritage are and have been members and/or leaders of the American Library Association (ALA). Some of them are also members of and participate actively in the National Association to Promote Library & Information Services to Latinos and the Spanish Speaking (REFORMA). The purpose of this case study is to understand how librarians of Latin American heritage (LLAH) experience a sense of belonging within librarianship based on their dual identity as REFORMA and ALA members. Through semi-structured interviews with eight LLAH who are members of and leaders in ALA and REFORMA and analysis of documents from both associations, we identified three main themes. LLAH are a diverse group, intersectional, from different ethnicities and cultural backgrounds with the common goal of serving Latino communities. In REFORMA, these diverse professionals balance their individual and social identities to find a community and a support system that helps the sense of belonging in a predominantly White profession. Findings from this study have implications for professional associations and their leaders who wish to make librarians who are Latine feel that they belong in LIS.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".