Beyond embedded: Blended roles for information professionals in the 21st century knowledge economy
Bibliographic record
Abstract
Twenty-first century librarians work with diverse populations with a variety of needs. Information users take multiple roles and institutional affiliations, collaborating across disciplines and domains of expertise. Librarians at the Gerstein Science Information Centre (Gerstein) of the University of Toronto Libraries (UTL) in Ontario, Canada have embraced the challenge of providing services that not only expand across traditional boundaries of public, academic and corporate information services, but also reach beyond a local user base. They meet this challenge by taking on embedded and blended roles as market intelligence information specialists at the MaRS Discovery District (MaRS), an innovation centre that works closely with the University of Toronto community. Working as a cohesive team, information specialists and industry analysts at MaRS serve clients that have diverse information needs, which span multiple industry sectors: information, communications and entertainment; life sciences; clean-tech and advanced manufacturing & materials; and social innovation. In the absence of a physical library, the market intelligence team leverages proprietary electronic subscriptions and publicly available information to deliver quality information services in a unique and sustainable way. This chapter will provide a case study that examines the growth of the operation from a single information specialist to a larger team over time, explains the current structure of the team, and explores the complementary strengths and skill sets of the various team members.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.036 | 0.035 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".