Adversative Discourse Markers in Kurdish Literary Texts
Bibliographic record
Abstract
Discourse Markers are one of an uninvestigated aspect of language in old and modern Kurdish linguistics, that has not been given due attention, neither by native nor non-native researchers. On this ground, it is hoped that the present study sheds light on this almost entirely ignored aspect of the language and this study is meant to be a systematic treatment of this group of lexical items known as Discourse Markers (henceforth, DMs), more specifically one category of them; Adversative DMs. DMs are words, phrases and even clauses that enhance discourse coherence and are found in all languages, as tapped on by researches and investigations. Numerous terminologies are utilized to refer to such group of markers by different researchers in English and other languages, such as ‘Discourse Particles, Cue Phrases, Small Words, Pragmatic Markers, Discourse Connectives… and even they are defined differently. It is postulated that DMs are meaningless and lay outside the domain of sentence structure. Likewise, lexical expressions that have different grammatical functions such as ‘and, also, but, or, simultaneously, at the same moment …etc, can also function as DMs to connect the previous utterance with the upcoming discourse segment. The current investigation endeavors to answer certain specific questions: first, the extents to which DMs are operated in literary texts; second, discourse functions DMs implement. Thirdly, the word categories DMs are derived from, and to which extent Halliday and Hassan (1976)’s framework is applicable to Kurdish DMs? For achieving the aims, the researchers analyzed one of the contemporary novels of a famous novelist entitled ‘Xezlenûs w Bâxekâni Xejâł”. By applying Halliday and Hasan’s (1976) framework and also by taking insights from Fraser (2009), DMs are categorized into different classes. One of which is Adversative DMs, which are the concern of the present study. For obtaining the frequency of each marker, the data are scrutinized manually, since there are no corpus analysis tools that can facilitate such measurements. The study concludes that Adversative DMs are frequently used in selected Kurdish literary texts and that they are similar to those found in English in terms of derived grammatical categories, taxonomy, and they have different characteristics in terms of form, position and discourse functions. Withal, it has been arrived that Adversative DMs are of different kinds analogous to those investigated in English by Halliday and Hassan (1976).
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".