Bibliographic record
Abstract
Supposing is good, but finding out is better."-Mark Twain I became a librarian because I love research.Specifically, I love the process of finding things out.It almost doesn't matter what-if I have a sense that the answer is there to be found, I want to dive in and find it, as many of us do.As a reference librarian at heart, it matters to me that the result of a search will be of benefit to some or many people, but still, beyond that, I find the process intrinsically satisfying.I entered librarianship with a background in population studies and public health research.As I gained experience as a librarian, I began to engage professionally in various ways, including doing research.This paper outlines my process in becoming, and embracing my identity as, a librarian-researcher.It also offers possibilities for how all of us who work in libraries can take steps to incorporate this important focus into our work. My Research BackgroundI date the beginning of my research career to the moment that I began a life-changing course at the University of Pennsylvania called "Introduction to Demography."It would probably not be life-changing for anyone else, but it was wholly unexpected for me to be so intrigued by a subject.Demography was,
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 | 0.053 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.039 | 0.043 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".