Bibliographic record
Abstract
processes.This twinned perspective is common among those of us who study, critique, and build knowledge organization systems: we know our work is ultimately subjective and its imposition on reality will have felt effects beyond some convenience in information retrieval.Choosing the Canadian Journal of Information and Library Science (CJILS) is an apt embrace of the (un)reality of national borders.CJILS is a Canadian project supported by Canadian cultural heritage and academic initiatives, and housing this collection here ties us to a theory of nationhood with an uncomfortable fit to the theme of this research.Thanks in large part to those same supports, CJILS is a truly open access journal, setting no institutional or financial barriers to authors or readers.We hope that those reading this collection and those supporting the sustainability of the diamond open access model appreciate both the intent and inherent irony of this relation.In this issue, you will read three papers with a North American framework and locality-we continue to welcome conversations and explorations of research on this theme from outside this locale.Recognizing that diversity in nation building and classification building examples are required, we hope readers use this special issue as a starting point for more dialogue and theory development.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".