Scientific literacy, librarians and information literacy in the post-truth era
Bibliographic record
Abstract
Purpose The purpose of this paper is to challenge librarians to reconceptualize their professional self-image and practice so that it more closely aligns with the information science discipline that is part of the Masters of Library and Information Science degree. Design/methodology/approach This column is primarily theoretical and philosophical but also draws on the author’s observations of trends and patterns in both librarianship and changes in information needs in recent years. Findings Urgent, high-cost information needs created by COVID-19 and climate change coexist in a reality where technological change has made traditional librarian roles and functions less critical. By developing their information science skills and strengthening their professional identity as information scientists, librarians have the opportunity to address the urgent information needs of the day while remaining highly relevant professionals. Practical implications Librarians will need to strengthen their science-related skills and knowledge and begin to promote themselves as information scientists. Social implications Librarians are in a position to make a meaningful contribution to two of the most pressing challenges of the day, climate change and dealing with the COVID-19 pandemic. Originality/value This paper is relevant to all librarians at any stage of their career. It will help them to reflect on both their skillset and career path and to make any needed adjustments so that they can remain relevant in a volatile and demanding information environment.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".