Implementing the Critical Friend Method for Peer Feedback among Teaching Librarians in an Academic Setting
Bibliographic record
Abstract
Objective – The role of the academic librarian has become increasingly educative in nature. In this study, the critical friend method was introduced among teaching librarians in an academic setting of medicine and health sciences to ascertain whether this approach could be implemented for feedback on teaching of these librarians as part of their professional development. Methods – We used a single intrinsic case study. Seven teaching librarians and one educator from the faculty of medicine participated, and they all provided and received feedback. These eight teachers worked in pairs, and each of them gave at least one lecture or seminar during the study period. The performance of one teacher and the associated classroom activities were observed by the critical friend and then evaluated and discussed. The outcome and effects of critical friendship were assessed by use of a questionnaire. Results – The present results suggest that use of the critical friend method among teaching academic librarians can have a positive impact by achieving the following: strengthening shared values concerning teaching issues; promoting self-reflection, which can improve teaching; facilitating communication with colleagues; and reducing the sense of “loneliness” in teaching. This conclusion is also supported by the findings of previous studies. Conclusion – The critical friend method described in this study can easily be implemented and developed among teaching librarians, provided that there is support from the organization. This will benefit the individual teaching librarian, as well as the organization at large.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".