Library Impact on Student Retention is Often Not Well Documented or Communicated
Bibliographic record
Abstract
A Review of: Murray, A. L., & Ireland, A. P. (2017). Communicating library impact on retention: A framework for developing reciprocal value propositions. Journal of Library Administration, 57(3), 311-326. http://www.tandfonline.com/doi/full/10.1080/01930826.2016.1243425 Abstract Objective – Identification of trends in documenting and communicating library impact on student retention. Presentation of a framework of library stakeholders with examples of how libraries can communicate their value to each stakeholder group. Design – Survey and presentation of framework. Setting – Comprehensive universities in the USA. Subjects – 68 Academic library deans/directors. Methods – A survey on current methods of documenting and communicating library impact on student retention was sent to all 271 comprehensive universities with a Carnegie classification of Master’s level. The response rate was 25%. Emergent themes were identified using NVIVO for the qualitative data analysis. The six markets model was presented as a framework for identifying library stakeholder groups. Examples of reciprocal value propositions (RVP) for each stakeholder group were provided. Main Results – Analysis of the survey results identified a number of themes about documenting library impact on student retention: use of information literacy assessment, use of satisfaction or feedback instruments (eg: survey, focus group), library-use data, and lack of knowledge of methods. Several responses indicated the methods used for information literacy assessment were not a direct measure for documenting impact on retention. A few institutions piloted more direct methods by combining library use data and student success metrics. A number of institutions said they struggled with how to use library-use data to calculate library impact on retention. Methods for communicating library impact on retention included formal presentations, annual reports, annual assessment reports, informal communication, and none. Communication was often tied to documentation; if a library did not collect or document impact on retention, they were not able to communicate anything. The authors noted communication tended to be unidirectional rather than being a multidirectional discussion between the library and its stakeholders. Based on the six markets model, the authors identified six library stakeholder groups that would benefit from understanding library impact on student retention. The authors postulated that identifying these markets would allow the library to define value propositions for each market. The value propositions for each market would be reciprocal because value would be co-created when the library engages with each stakeholder group to fill a service need. The authors proposed that identifying and engaging with stakeholders, and defining reciprocal value propositions for each, would provide the library with an opportunity to advocate for itself.
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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 | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".