Bibliographic record
Abstract
Perlocutionary intertexts (PIs) have received little attention in the literature on nonliterary discourse. This paperexplores PIs in spiked article titles. A textual survey and intertextual analysis were conducted to probe into thepragmatic and intertextual workings of the PIs and their potential appeals. It is found that 1) the socioculturalknowledge for the activation of the PIs is primarily derived from formulaic language, prominentliterary/non-literary works, and media products; 2) pragmatic foregrounding attracts the reader’s attention; 3) thecommon ground derived from the source text forms the basis of the intertextual association; 4) externalintertextuality triggers the pragmatic inferencing of the PI meaning and/or significance; and 5) internalparatextuality confirms or further develops the pragmatic inferencing. In addition, a post hoc questionnairesurvey with 83 Taiwanese college students and their reading-response logs indicate that such intercultural PIs aremoderately appealing, with their repellent effects upon the unknowing readers nullifying some of their attraction.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".