An Interview with Reece Steinberg: Teaching and Learning in the Libraries
Bibliographic record
Abstract
In this interview, Reece Steinberg, Head of Library Learning Services and Business Liaison Librarian at Toronto Metropolitan University (TMU), shared insights into his extensive experience in library instruction and research support. With 17 years in librarianship, Steinberg has specialized in assisting entrepreneurs and business students while exploring topics such as the ethics of storytelling, library instruction, and the impact of neoliberalism on business research. He discussed his preparatory approach for teaching, emphasizing the importance of understanding course content and collaborating with professors to tailor lessons. Steinberg described the dual nature of his instructional work, which includes both recurring class sessions and one-on-one interactions, highlighting the value of diverse teaching methods to reach and support students. He shared practical advice on effective teaching, such as soliciting real-time feedback to adjust lessons. Reflecting on changes in instructional work, Steinberg noted the evolving impact of technology and anticipated shifts in assignment formats due to emerging tools like ChatGPT. His insights offer a comprehensive view of current trends and future directions in library instruction.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".