Bibliographic record
Abstract
Sweetman, Kimberly Burke. Managing Student Assistants: A How-to-do-it Manual for Librarians. New York, NY: Neal-Schuman Publishers, 2007. 187 p. $59.95 USD. ISBN-10: 1-55570-581-2; ISBN-13: 978-1-55570-581-7.∞ Without a doubt, finding enough people to a library can be a daunting task. Never mind the professional librarian positions; there are many jobs in any academic library that must be filled, each with a range of tasks and skill requirements. Given the location of such libraries, employing students is not simply an obvious choice; in most cases it is the only choice. Kimberly Burke Sweetman, Head of the Access Services department for New York University's Division of Libraries, is out to dispel what she sees as a myth that workers, being such a transient population, do not have much to offer (5). Hoping to guide the harried librarian through what Sweetman views as a minefield of potential problems, she lays out constructive advice on not only how a student should be hired, but why. Unfortunately, despite her best intentions to present the management of students as unique, the overall result in Managing Student Assistants is one of redundancy. Sweetman begins by discussing the history of using students in a library setting, going back to articles in the 1910 ALA Bulletin, which advise the use of students for routine tasks such as book repair and filing. Going through the decades, she sees a major shift in the 1970s, as hiring students becomes seen as a way to improve the cultural diversity of library staff (2). Coming close to the present, there is widespread and accepted use of students for a myriad of tasks, but the stigma remains that using students can often be more trouble than it's worth. While the impetus to encourage the hiring of students is admirable, Sweetman risks negating her position by suggesting that categorizing mass numbers of people by the time in which they were born can ease a manager's woes. Quoting several studies, Sweetman lays out the idea that Baby Boomers?are inner driven and competitive (13), and members of Generation X are independent workers who don't trust authority, consider loyalty naive, and are loyal to projects and teams as opposed to companies (14). By contrast, the Millennials (those born between 1981 and 1999) are outer-driven team players who exhibit good fellowship. This culturally diverse group trusts authority, is intensely loyal, and?desires continual feedback (14). Sweetman posits that altering policies and techniques to better manage such groups is essential to good management. Certainly, there are shifts in mindsets and attitudes over the years that any good manager must accept and adapt to in order to keep a library functioning smoothly. …
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.151 |
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".