Something to Talk About: Re-thinking Conversations on Research Culture in Canadian Academic Libraries
Bibliographic record
Abstract
As Canadian academic librarians have experienced an increasing presence in faculty associations and unions, expectations of librarian scholarship and research have increased as well. However, literature from the past several decades on academic librarianship and scholarship focuses heavily on obstacles faced by librarians in their research endeavours, which suggests that the research environment at many academic libraries has stalled. Though many have called for the development of a research culture, little has been said regarding how the profession might go about encouraging this development, and conversations often become mired in the contemplation of obstacles. As a way to move forward, we suggest building upon pre-existing strengths by adopting the model of “intellectual communities” put forward by Walker et al. They describe four qualities necessary for strong “intellectual communities”: shared purpose; diverse and multigenerational community; flexible and forgiving community; and respectful and generous community. Although these qualities are often embedded within our libraries, they need to be made a conscious part of our research environment through reflection and conversation. Working toward strong research cultures requires that we focus less on obstacles and more on reflective and productive activities that build on our strengths.
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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 | 0.065 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.124 | 0.074 |
| Scholarly communication | 0.036 | 0.026 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.011 | 0.022 |
| 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".