Advocating for Librarianship: The Discourses of Advocacy and Service in the Professional Identities of Librarians
Bibliographic record
Abstract
A dedication to service is often cited as a hallmark of a profession. Service is included as one of eleven Core Values in the American Library Association’s “Core Values of Librarianship” (2004). For librarians, service includes helping people find information resources to meet their educational, recreational, and work needs. Reporting findings from a larger study into the professional identity of librarians, this paper explores the centrality of service, with specific attention to how librarians advocate for their services and, ultimately, for librarianship. Using a discourse analysis approach, this study examines the roles that Service as a Core Value and advocacy play in the construction of professional identity. Three different data sources were used: professional journals, e-mail discussion lists, and research interviews. The data were analyzed for the discourses librarians use when describing librarians, librarianship, and professionalism and their connection to advocacy. When librarians advocate for the services they offer, they are in fact advocating for the value of the profession. Discursively, speaking or writing about advocacy positioned librarians as active participants in their own identity formation. By making advocacy a central activity of the profession, librarians not only challenged others’ perception of librarianship, they challenged their own understanding as well.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.033 | 0.051 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".