Bibliographic record
Abstract
In this poster, we report preliminary results from a survey of academic librarians in the U.S. and Canada on their perception of data literacy for their work practice. Our survey was distributed in August 2022 to librarians from member libraries of the Association of Research Libraries, Canadian Association of Research Libraries, Oberlin Group, as well as through various LIS listservs. We received 338 valid responses. Our focus is on the impact of library membership, librarians’ educational backgrounds, and percent of data work on librarians’ perception of the importance of various areas of data literacy. Significant library membership differences occurred both in the percentage of their job involving data-related tasks (H(3) = 9.146, p = .027), with ARL librarians having the highest mean rank, and in respondents’ importance rating on research data principles (H(3) = 10.534, p = .015), with CARL librarians having the highest mean rank. Librarians who had a non-MLS degree rated their data proficiency as significantly higher than respondents with MLS and another degree and people with only an MLS degree (H(2) = 8.815, p = .012). Percentage of data-related work was positively correlated with self-rated data proficiency (rho(n = 253) = .641, p = .000), the level of data literacy needed (rho(n = 262) = .352, p = .000), and various importance ratings of data literacy areas including programming skills (rho(n = 251) = .268, p = .000) and data processing & visualization (rho(n = 253) = .185, p = .003). Further analysis is currently in progress.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".