Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<em>Slides for STI 2023 Special Session: Metrics literacy</em> <strong>Session Objectives</strong> 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. <strong>Outline of Session</strong> 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 <br> *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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.025 |
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 it