Bibliographic record
Abstract
In August 2011 the Simon Fraser University Library launched a new IR, Summit, based on the open source Drupal content management system. Prior to this, IR Services had been very low profile due to the perceived lack of flexibility of the current IR platform, lack of extensive Java programming skills and our inability to accommodate enhancement requests from faculty.\nMoving to Drupal allows the SFU Library to take advantage of the global pool of Drupal developers and our in-house Drupal expertise. For example, only three modules were written from scratch while the other seven required modules were modified ones already developed by Drupal developers. As a consequence of this change of platform, we now have a dynamic and flexible platform that allows us to collaborate with faculty on enhancements to develop more responsive IR services. Faculty requests for enhancements drive IR development: examples include small tweeks like adding required HTML metadata headers to ensure indexing by Google Scholar and export of records to Zotero bibliographic software; modifications of author pages; giving users the ability to version documents; restricted collections for purposes of document sharing and pre-publication work; record display enhancements; search result enhancements; ongoing development of ways to synch collection content in the IR with departmental websites. These enhancements help to bring in new users.\nAs well, the SFU Library’s new Scholarly Digitization Fund, where the Library pays for digitization of faculty research, has brought in non-traditional IR users; provided a further opportunity to promote OA; and broadened the Library’s scope of what belongs in an IR.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.028 |
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".