Bibliographic record
Abstract
Studies (FIMS) at the University of Western Ontario invites applications for a full-time, probationary appointment (tenure-track)at the rank of Assistant Professor to begin July 1,2003.Candidates must have a Ph.D. completed or nearing completion in Library and Information Science or related area and show evidence of strong research potential and excellence in teaching.Professional experience as a librarian or information manager in a traditional or nontraditional setting is an asset.This advertisement is directed to individuals who have teaching and research interests in the area of information technology and systems, including questions at the intersection of information, technology, and users.The normal teaching workload in FIMS is four half courses per academic year.The successful candidate will demonstrate the ability to contribute to the faculty's programs, especially the master's and Ph.D. programs in Library and Information Science.Expertise in one or more of the following areas is desirable: Information systems and architecture; interface design and usability; digital libraries; health informatics.The faculty of Information and Media Studies is a vibrant, expanding faculty of more than 35 full-time faculty members and 17 nonacademic staff.It currently offers an undergraduate program in Media, Information, and Technoculture with an enrollment of 700 students, as well as a master's in Journalism, a master's and doctoral program in Library and Information Science, and a new graduate program in Media Studies.Information about the faculty and descriptions of our programs are available at: http:// www.fims.uwo.ca.The University of Western Ontario is a research intensive university of 27,000 full-time equivalent students.Interested candidates are invited to send their curriculum vitae, names and ad dresses of three references, copies of their scholarly writing, and a cover
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.848 | 0.804 |
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".