Integrating Social Work Perspectives into LIS Education: Blended Professionals as Change Agents
Bibliographic record
Abstract
Abstract Purpose – In this chapter, I present a systematic discussion of the relationship between social work (SW) and library and information science (LIS) and explore how SW can contribute to the education of LIS practitioners so that they become more than information facilitators and grow professionally to be true agents of change. Design/Methodology/Approach – Using engagement with immigrant communities as a case in point and building on the empirical comparative study of public librarians in the Greater Toronto Area and New York City, I outline the current gaps and deficiencies of LIS curricula that can be rectified through blended education. I also integrate the potential contributions of SW into LIS through the case study of an immigrant member of a library community. Findings – Building on the case study, I introduce a four-tiered model that can be applied to a wide array of courses in LIS programs and conclude with suggestions for taking steps toward blending SW perspectives into the LIS curriculum. Originality/Value – I position the potential fusion of SW and LIS as “professional blendedness,” which serves as a catalyst for change, and also examine the concept of the blended professional as a change agent. I introduce the rationale for adopting theoretical, practical, and pedagogical approaches from SW in the field of LIS and focus on four specific contributions that can most benefit LIS:
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".