Bibliographic record
Abstract
This essay is adapted from a lightning-talk presentation given at the annual conference of The Workshop for Instruction in Library Use (WILU), held at the University of British Columbia, May 30-June 1, 2016. The conference theme was Intersections. The presentation and essay highlight the emerging library partnerships between Laurentian University's McEwen School of Architecture Library and various groups in downtown Sudbury, Ontario that are leading to expanded services and positive community engagement. Cet essai est une adaptation d’une présentation « éclair » offerte lors du congrès de l’Atelier annuel sur la formation documentaire, plus communément connu sous son acronyme anglophone WILU (Workshop for Instruction in Library Use), qui a eu lieu à l’Université de la Colombie-Britannique du 30 mai au 1er juin 2016. Le thème du congrès était « Intersections ». La présentation et cet essai portent sur des partenariats en émergence entre la bibliothèque de l’École d’architecture McEwen de l’Université Laurentienne et divers groupes situés au centre-ville de Sudbury, Ontario qui mènent vers une prestation élargie des services et un engagement communautaire positif.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.048 | 0.047 |
| Scholarly communication | 0.044 | 0.025 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".