Research Supports are Effective in Increasing Confidence with Research Skills in Early Career Academic Librarians
Bibliographic record
Abstract
A Review of: Ackerman, E., Hunter, J. & Wilkinson, Z. T. (2018). The availability and effectiveness of research supports for early career academic librarians. The Journal of Academic Librarianship, 44(5), 553-568. https://doi.org/10.1108/ILS-09-2016-0068 Abstract Objective – To identify the type and efficacy of research supports currently available to early career academic librarians. Design – Survey. Setting – The United States. Subjects – 213 academic librarians who were not yet promoted or have received tenure, or those up to three years post-tenure or promotion. Methods – The researchers created a survey containing 39 closed and open-ended questions using the software Qualtrics. The question types included multiple choice, Likert scale, and free text. The survey was distributed through direct emails and various professional electronic mailing lists. Main Results – The majority of respondents listed finding time as the most significant barrier to conducting research. Respondents listed informal mentoring as the most commonly used and most widely available form of research support. Statistical analyses revealed that for every type of research support a librarian engaged in, on average confidence increased by 0.10. Conclusion – Engagement in formal and informal research supports may influence early career academic librarians’ confidence levels in regards to conducting research projects. Academic institutions as well as professional organizations should ensure that ample opportunities are available.
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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 | 0.031 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".