Bibliographic record
Abstract
In today’s world of digital scholarly publishing, it is increasingly clear that movements such as open access (OA), Open Science, and open peer review (OPR) are increasingly impactful and gaining momentum. The shift towards openness in the academy reveals a transformation of traditional structures that compose scholarly communication as well as changing attitudes about the nature of authority and access within these systems. These new directions in the scholarly information landscape have created a need for academic librarians to realign roles and respond in ways that build resiliency in an era of rapid change. Recognizing that many core elements of scholarly communication are powerful tools for teaching students about information literacy can lead to transformative instructional strategies. This paper explores how academic librarians can leverage the innovative traits of OPR to advance information literacy through experiential student learning opportunities grounded in the ACRL (2016) Framework for Information Literacy for Higher Education.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.080 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".