The Organizing of Scientific Fields: The Case of Corpus Linguistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it