Bibliographic record
Abstract
In this article, the authors take a critical look at the challenges faced by librarians new to the profession. Using Michel de Certeau’s The Practice of Everyday Life, they examine how the coping tactics for new librarians described in the literature mirror the “tactics” de Certeau describes individuals using to “make do” within systems of power. This includes how individuals make do in their personal lives by coping with stress and how they succeed at work by developing networks and manoeuvering politically. This article will also discuss what de Certeau refers to as “strategies,” or moves undertaken by the system of power itself, within the context of onboarding and mentorship. Examining the literature on new librarians through the lens of de Certeau allows the authors to consider the ways in which the individual may choose to act tactically and integrate into a system of power, or may use the limited means available to them to quietly rebel. It also allows the critique of the use of tactics, which require individuals to take on additional work in order to adapt to the needs of the institution. Requiring new librarians to adapt to their institution is problematic, specifically in relation to precarious labour and to barriers to entry. These systematic issues require a collective response beyond what individuals can address. Using de Certeau’s work, the authors critically examine the literature that exists on new librarians and how individuals adapt and change to the needs of the academic library as a system of power.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.064 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".