Bibliographic record
Abstract
Scholars’ interest in the speech act of complimenting has grown over the years for many reasons one of which is its being robust with sociolinguistic information. However, it appears that many studies still grapple with what the compliment is because of its nature which has been described to be naturally ubiquitous and many times overlapping with other speech functions. In addition, fieldwork experiences have also called for the need to define the compliment although some scholars have argued that there is no need to define the act. In this paper, we glean from fieldwork experiences and data analysis in a study of 1200 compliments in Nigerian English to support the claim that a definition is needful and to propose some defining characteristics (termed approaches) that might be useful for those researching on the compliment data.
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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".