Bibliographic record
Abstract
Abstract Traditional field delineation methods in bibliometric studies appear to have reached their limits when dealing with highly interdisciplinary fields such as nanotechnology or stem cell research which have recently become a focus of science and technology policy research. Researchers therefore have developed sophisticated algorithmic procedures to overcome these difficulties, hoping to collect a set of articles in a research field being studied that is complete as well as clean. The present case study explores the effect of field delineation on author co‐citation analysis (ACA) studies of the intellectual structure of the library and information science (LIS) field using two different but overlapping journal sets to define the LIS field. We find that the major overall structure remains largely the same between these two views of the LIS field, which suggests that field delineation is not crucial to ACA studies of research fields, provided the emphasis of a study is on the major overall structure of a research field. The two views do however differ at a more detailed level of analysis, which suggests that studies that aim to shed light on particularly subtle research policy issues may need to pay serious attention to the way that they delineate their fields.
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 | 0.082 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.036 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".