From paper to electronic, the evolution of pathfinders: a review of the literature
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a review of the literature on pathfinders, from the 1970s to the present. Design/methodology/approach The paper reviews a range of publications which describe the methodology of pathfinders, provide practical advice and information, present research results, to aid librarians and library administrators in how to best manage the production and marketing of pathfinders. Findings It was found that not much has been written on pathfinders. A few articles on traditional pathfinders were published between 1972 and 1995. In 1996, the electronic format took over. Most of the articles are of a practical nature although some describe empirical research. One void in the literature that has been found is librarians' lack of knowledge of users' needs and preferences. This results in much time and effort being dedicated to the production of pathfinders but without any consideration of users, thus discouraging them from using the available resources. Practical implications This paper will be a useful source of information for librarians. It provides an overview of guidelines and best practices currently reported in the literature as well as the latest technical and educational trends. Originality/value Such an extensive review of the literature on pathfinders has not been done before. It provides practical information for librarians wanting to embark on the production of pathfinders. It also identifies possible areas of future study.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".