Evidence Based Library and Information Practice EBL 101 A New Path: Research Methods
Bibliographic record
Abstract
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one. For more than two years, this column has taken you through the steps of evidence based library and information practice (EBLIP). With the final step taken in the last issue (keeping in mind that EBLIP is an iterative process and that the notion of the final step is sometimes interpretive), it is time to choose a new path and take the first step on an exciting new journey. For the next while, I will explore the exhilarating world of research methods! Do I sound invigorated? I am! I’m no expert, by any means. But, I am a lifelong learner; a practitioner-researcher with a strong interest in research methods, so we are going to learn together. And of course this column is EBL 101, so the information will be introductory and by no means exhaustive. As of right now, I have no set plan on the exact methods I will tackle, nor the order in which I will wrestle them to the ground. So if you have any needs or suggestions, please let me know. For this first column on our new path, I’m going to talk about qualitative and quantitative research in general. Yes, that is a big topic for a small column, so let’s see how it goes. The rivalry between the Toronto Maple Leafs and the Montreal Canadians (hockey for the non-Canadians on board) has nothing on the rivalry between quantitative and qualitative research methods, or at least between the researchers devoted to them. Qualitative scholars consigned quantitative research to the lower echelons of the scientific field because of its “subjective, interpretive approach ” (Denzin
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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 | 0.065 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.053 | 0.031 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.276 | 0.270 |
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".