A two-way street: building the recruitment narrative in LIS programs
Bibliographic record
Abstract
Purpose This paper aims to explore the attractiveness of Library and Information Science (LIS) careers to students and alumni and examine their decision-making process and perceptions of the field with an eye on discerning the best ways to build and develop the recruitment narrative. Design/methodology/approach The authors reached out to 57 LIS graduate programs in Canada and the USA accredited by the American Library Association through a Web-based survey; the questions presented a combination of multiple-choice, short-answer and open-ended questions and generated a wealth of quantitative and qualitative data. Findings The online survey has disclosed that students may not have an in-depth understanding of current trends, the diversity of LIS professions and the wider applications of their education. A significant disconnect exists in how the goals of LIS education are seen by certain groups of practitioners, students and faculty members. Originality/value Creating a program narrative for the purposes of recruitment and retention, departments should not only capitalize on the reach of the internet and the experiences of successful practitioners. They should also ensure that faculty know their students’ personal backgrounds, that students empathize with demands of contemporary academia and that a promotional message connects pragmatic educational goals to broader social applications. By exposing and embracing the complexity of LIS education and practice, the paper chooses a discursive path to start a conversation among major stakeholders.
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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 | 0.101 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".