Specific Features of Professionally Oriented Written Communication in English of IT Specialists
Bibliographic record
Abstract
The article reveals the peculiarities of professionally oriented written communication in English of IT specialists.In the framework of the main situations of professionally oriented written communication in English (business correspondence, correspondence with colleagues and chiefs) the functions of the communication realize: contact, information, inducing, cognitive, emotive, influencing and fence-mending ones.The genres of professionally oriented written communication in English of IT specialists include: private and business e-mail correspondence, texting in chats and forums, posting on electric announcement boards, memo, and reports.The main types of this communication are informal (communication with colleagues), semi-formal and formal (communication with suppliers, clients, chiefs), analytical and scientific communication (reports).Each type of communication has its peculiar stylistic feature concerning lexical and grammatical presentation.Key words: professionally oriented written communication in English, IT specialists, functions and duties, correspondence, genres, types of communications.According to the purpose, the objectives of the article are as follows:1) to outline the list of specialties which higher educational institutions in Ukraine follow to provide training of prospective IT specialists, and also the range of professional functions and duties of IT specialists; 2) to analyze the essence of key concepts: "communication", "written communication", "written English professionally oriented communication of IT specialists"; DOI 10.32370/2018_11_13 3) to ascertain situations, functions, genres, types, features of professionally oriented written communication in English of IT specialists.Nowadays F. Batsevych, O. Selivanova are researching the issues of communicative linguistics and intercultural communication.The study of professionally oriented communication of non-philological specialties has received a wide attention in the research of S. Kozhushko, N. Mykytenko, M. Kozolup, V. Sulym, O. Tarnapolskyi and others.M. Castelss, O. Dedova, D.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".