The Architecture of an Information System for Public Relations via Mobile Application Using In-depth User Experience for Proactive Perception of Information
Bibliographic record
Abstract
The objectives of this research were as follows: 1) To study, analyze, synthesize documents and researches related to the architecture of an information system for public relations via mobile application using in-depth user experience for proactive perception of information, 2) Design the architecture of an information system for public relations via mobile application using in-depth user experience for proactive perception of information, 3) Develop the architecture of an information system for public relations via mobile application using in-depth user experience for proactive perception of information, and 4) Study the results of evaluating the suitability of the architecture of an information system for public relations via mobile application using in-depth user experience for proactive perception of information. The samples used in the research were 20 experts in information system development from various institutions in higher education. The assessment results found the following: 1) The developed system architecture has four components: stakeholders, user experience process, output and feedback, 2) The results of the evaluation of the suitability of the developed system architecture found that (2.1) the assessment results of the suitability of the developed system architecture (aspect of integrated components) was appropriated at a high level (Mean=4.43, S.D.=0.67) (2.2) the assessment results of the suitability of the developed system architecture (issue by separated components) were appropriated at a high level (Mean=4.37, S.D.=0.80), and 3) The assessment results of the suitability in implementing the developed system architecture were appropriated at a high level (Mean=4.17, S.D.=0.74), respectively.
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".