The Organizing of Scientific Fields: The Case of Corpus Linguistics
Bibliographic record
Abstract
This paper focuses on the processes through which scientific fields are organized over time. It is argued that new approaches in scientific work are hampered by authority structures within national systems for research and established approaches within disciplines, but that these obstacles can be overcome by means of external funding, particularly through new funding sources, as well as the international developments of an innovation. As far as the latter are concerned, they are expected to first lead to informal collaboration among scholars. In the passage of time this informal collaboration becomes more and more formalized. In order to analyse such processes the paper presents a model with three phases labelled as creating, gathering and communicating. This model is then used in an empirical study of corpus linguistics, i.e. the systematic analysis of well-defined populations of written and/or spoken language material. It is shown in the paper how corpus linguistics was developed by scientific innovators who were initially questioned. With the passage of time they created a number of international organizations, which have eventually become more and more formalized, many of them publishing their own journals. In this way the paper demonstrates the significance of organizing for the development of scientific fields.
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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 | 0.058 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".