Librarians and Computer Programming: Understanding the role of programming within the profession of librarianship
Bibliographic record
Abstract
Computer programming is increasingly being discussed as a practice within librarianship. However, contemporary discussions about the role of code within librarianship often suggest that librarians should or should not learn code while failing to qualify how and why librarians are employing code in a professional capacity rather than IT professionals. By investigating case studies that describe librarians writing code, this paper qualifies popular discussions of code and librarianship with how and why programming is being used in practice by librarians. While these case studies reveal that programming solutions were developed in the context of lack of IT staff, librarians are not taking over roles or projects traditionally afforded to software and web developers, instead librarians are writing code for data processing and web services as extensions of their normal responsibilities. Further discussion explores software engineering as the primary concern of librarians who code professionally.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".