Planning for Transformation: A Semantic-Grammatical Based Discourse Analysis of Saudi Vision 2030
Bibliographic record
Abstract
The need for transformation has led kingdom to envision and encode the Saudi Vision 2030 document; it is not merely an idealistic divination but a manuscript with an appropriate plan to accomplish its anticipated economic and social goals.In fact, planning is a critical factor in the document, which shapes it as a discourse of realization and fascination, made in the public interest.The present research aims to investigate the ways strategic planning has been articulated in the Vision 2030 document.It can help to get a deep linguistic understanding of this ideological discourse as well as to make it comprehendible for familiar readers.The core purpose of the present study is to examine this ideological discourse for the linguistic items that encapsulate the planning factor.For this purpose, the text has been reviewed using the foundational document model projected by Holland (2014).This semantic-grammatical based linguistic model helps to investigate the ideological strand of planning in the selected text.The research design is quantitative, using a content analysis method.The results reveal that planning strategies are well voiced in the Vision 2030 document, using a variety of vocabulary items; Investment, support, cooperation, provision, and increment are found as the fundamental strategies.The study suggests that other linguistic features can also be investigated to explain this document in the public interest.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".