Bibliographic record
Abstract
Slides for STI 2023 Special Session: Metrics literacy Session Objectives This hands-on session invites conference participants to actively engage in the community-driven development and discussion of metrics education as a new focus area of bibliometric research. The session aims to bring attention to and empower the bibliometric community to take ownership of metrics education. Improving metrics literacies with the goal of reducing the misuse of bibliometric indicators is in line with current transitions towards a healthier academic culture, including the Coalition for Advancing Research Assessment (CoARA) initiative. The session will work as an incubator of ideas on how to effectively and efficiently communicate the knowledge of bibliometric experts to the broader audience of users of scholarly metrics. Using design thinking, we will consider user perspectives to empathize and understand users to more effectively identify problems encountered by individuals in the current metrics system. We also hope it can facilitate collaborations between various stakeholders, including bibliometric researchers and analysts, data providers and librarians. Outline of Session 16h00 Introduction: Metrics literacies and design thinking 16h15 Hands-on breakout session: Design thinking exercises in small groups* 17h20 Wrapping up: Reporting back and closing *Participants will be asked to organize in small groups of people with similar backgrounds and roles with regard to bibliometric indicators (see back of paper for instructions).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.632 | 0.412 |
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".