Bibliographic record
Abstract
This paper proposes solutions for a number of difficulties that Swalean generic structure analysis has been experiencing concerning the identification methods it applies to generic structure components. It does this by integrating the Greimassian method, a structuralist method of reducing elements to the minimal units of signification. In making this proposal, it points out that the underlying goal of Swalean genre analysis, which aims to classify various academic elements that differ in their degree of prototypicality into an umbrella of family resemblance, can be considered analogous to the structuralist goal of reducing elements to the minimal units despite the different theoretical groundings of these two approaches. It demonstrates the compatibility of these approaches by reducing some of the components of the Creating A Research Space (CARS) model; namely, it reduces Move 1 and Move 2 into one unit. Additional emerging elements that are similarly complex in academic writing, such as postmodern personal anecdotes, can also be sorted out into this unit. It concludes that methods in semiotics may offer useful frameworks for the applied disciplines where signification processes need to be revealed in analysis.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".