Meet ESiLS—The Empirical Studies in Libraries Summit
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
The inaugural Empirical Studies in Libraries Summit (ESiLS) occurred this past March, culminating months of careful-and fun-brainstorming and planning.As its founders, we want to share the story of how this conference came to be in this editorial, while also making space to express our excitement about the articles in this EBLIP issue resulting from ESiLS sessions and posters.At its heart, what became "ESiLS" could have begun when we met through our respective doctoral programs at the University at Buffalo (Logan's in Learning and Instruction; Laureen's in Information Science).But first we became friends and colleagues, individuals who respected each other's experiences, skills, and personalities.We are both practitioner-scholars working in academic library settings, roughly at the mid-career stage.We are both individuals choosing to pursue doctoral degrees as part of our own professional advancement and hoping to make contributions not only in our daily work as practitioners but also through our scholarly endeavors.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.378 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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