Relational practice of Canadian academic librarians: Exploratory content analysis using relational-cultural theory
Bibliographic record
Abstract
There is increasing recognition that relational practice, such as building community, empathy, and team building, is an important facet for Canadian academic librarians. This study measures the visibility of relational practice of Canadian academic librarians. Job postings from The Partnership Job Board were collected for a six-month period beginning in July 2021. Job postings were analyzed using deductive content analysis, utilizing a framework based on relational-cultural theory (Arellano Douglas & Gadsby, 2019; Fletcher, 1998). The framework identifies relational practice as falling into four categories: preserving, mutual empowerment, emotional strategizing, and creating team. While there is mention of relational practice in the job postings, further visibility is needed to accurately reflect this aspect of Canadian academic librarian work. Emotional strategizing (51.7 % of job postings with one or more mention) and creating team (47.2 %) are the most visible relational practices, while preserving (7.9 %) is hardly mentioned. There is far less mutual empowerment for practicing librarians (9 %) compared to librarians in management positions (57 %). The visibility of relational practice increases as the highest credential offered at the post-secondary institution increases, however this trend is not seen for mutual empowerment at Canadian Ph.D./doctorate-granting institutions. There is a need to increase visibility of relational practice with more frequent inclusion in academic librarian job postings and job descriptions, inclusion in the review, tenure, and promotion process, and identification together with clear communication of the value of relational practice.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".