Multicultural Competency Training Model for Digital Publicists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study is to develop and evaluate a multicultural competency training model for digital publicists. It is research and development which the researchers have divided the research process into 3 steps. Step 1: Developing a multicultural competency training model for digital publicists. Step 2: Evaluating the suitability of the developed multicultural competency training model for digital publicists. Lastly, step 3: Adapting a multicultural competency training model to developed digital publicists. Furthermore, the study found that the multicultural competence of digital publicists consisted of 4 competencies: 1) Digital Engagement Competency, 2) Dissemination Competency, 3) Facilitation Competency, and 4) Consulting Competency. Moreover, the results of the developed multicultural competency training model adaptation for digital publicists can be divided the assessment into 3 areas including, 1) Knowledge: the sample group has higher scores after training, 2) Skill: the sample group has the overall multicultural digital public relations skills at an excellent level, and 3) Attitude: overall, the sample group has a superb attitude.
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.
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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it