Data Literacy Proficiency and Its Importance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it