An Analysis of Generic Features of Acknowledgments in Academic Writing: Native Speakers of English vs. Non-Native (Iranian)
Bibliographic record
Abstract
The literature on the generic structure of acknowledgment has revealed that, beyond the role it plays in academic gift giving and self-presentation, the textualization of gratitude reveals the effect of disciplinary, sociocultural and contextual variations on shaping this genre (Hyland, 2003; Giannoni, 2002; Yang, 2012). However, there is relatively scant research on the ways that acknowledgements in different genres are characterized by their distinctive communicative purposes. To fill this gap, this study analyzes through two phases the acknowledgment sections of various genres (20 MA & 20 PhD theses, 20 textbooks, and 20 research articles) written by native speakers of English (n=40) and Iranian (n=40) in applied linguistics. The results of move analysis phase which insights was from Swales’ (1990) model, showed that genre of acknowledgment was constituted of a main “Thanking” move framed by two optional “Reflecting” and “Announcing” moves in theses, two optional “Framing” and “Announcing” moves in textbooks, and one optional “Framing” move in research articles. Despite observing the “Thanking move” in acknowledgment sections of all genres, cross-generic differences were also found in the type and frequency of constituent steps used to realize this move and other optional moves. These differences indicate how the contextual, cultural, and institutional forces influence the production and reception of academic genres.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".