Microaggressions as a Barrier to Effective Collaboration Between Teaching Faculty and Academic Librarians: An Analysis of the Results of a US and Canadian Survey
Bibliographic record
Abstract
Facilitating effective collaboration with teaching faculty (TF) for the purposes of student success and performance is often a priority for academic librarians (AL). The topic of effective partnerships between these two groups has received a great deal of scholarly attention within the field of library and information science (LIS). However, in practice, harmonious working relationships can be difficult to establish and maintain. This is in part due to the lack of understanding of the role and status of AL by TF. The existing divide between these parties has led to discourse and dismissive actions on the part of TF that may be perceived by some AL as microaggressive. While some work has been done on microaggressions in higher education, little quantitative data exists on status-based microaggressions by TF towards AL and its effect on collaboration in the context of information literacy (IL). In early 2016, the researchers surveyed U.S. and Canadian AL in order to collect data on perceived status-based microaggressive experiences. Analysis of the data indicates that status-based microaggressions, although not ubiquitous, do exist. Moreover, the data indicates that some librarians may experience more frequent instances of status-based microaggressions based on self-reported demographic characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".