Developing the Knowledge Workers Model for Core Competencies Management in Iraqi Higher Education Institutions
Bibliographic record
Abstract
This paper aims at developing the knowledge workers model for core competencies management via identifying the dimensions of the knowledge workers that are possibly related to the core competencies management. The primary motivations for the current research lies in those gaps represented of scientific and experimental studies in this scope, as well as for the purpose of increasing knowledge in this field. This theoretical research contributes to the development of the knowledge workers model based on its dimensions for core competencies management in Iraqi higher education Institutions. This research used quantitative approach by questionnaire was taken in collecting the data from the research community represented by some Iraqi higher education institutes samples that reached (256) questionnaires, which is about (80%), distributed to individually. The correlation coefficient (Spearman's) and Regression coefficient was relied on by using spss-ver.24. also the Knowledge-based Institutional theory was depended on explaining the results. The empirical analysis of the results was made using (Cronbach's alpha) to test the scales consistency of the validity of the consistency coefficient. The questionnaire was on a high consistency and validity. The results have largely supported the research model referring to the relationship of the knowledge workers have a good correlation and influence relationship in the core competencies management. Hence, this research could be of great use to the researchers, academics, professionals and policies makers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".