Bibliographic record
Abstract
In academic libraries, library instruction often takes the form of one-shot instruction and is not always deeply linked to the broader curricula. This article will argue that if library instruction in an academic setting is to be perceived as beneficial to student learning, it needs to coalesce around a set of specific values and teaching practices. To do so, this article will build on scholarship about signature pedagogies to continue the work of identifying a signature pedagogy for academic libraries engaged in information literacy instruction. To Lee S. Shulman, signature pedagogies outline the teaching practices used to educate new professionals, and they are essential to understanding how a profession develops. By participating in the process of developing a signature pedagogy, academic librarians would be able to engage in questions about the nature and purpose of their work, as well as set a foundation for how practicing and developing library professionals are educated and continue to be educated.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.103 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".