Analysis of Islamic Banking Study Program Students' Strategy to Become Banking Employees
Bibliographic record
Abstract
The aim of this research is to determine the strategy for Sharia Banking Study Program Students to become Banking Employees. This research is field research, using a descriptive qualitative approach. The data sources used in this research are primary data and secondary data. The data analysis technique used is data reduction, data presentation and drawing conclusions. This research data collection used observation, interview and documentation techniques. The research results show that students have significant opportunities, such as Islamic religious background, knowledge of sharia banking products, product marketing skills, and the application of morals in everyday interactions. On the other hand, threats to students include banks' lack of priority towards graduates with a sharia banking educational background, recruitment policies that prioritize physical appearance, and limited information about available recruitment. To overcome this challenge, students can apply strategies such as utilizing experience and knowledge from lectures and field work practices, increasing understanding of sharia banking principles through organizational activities to train public speaking, seminars, partnership program training from sharia banks for students, and being active in seeking recruitment through social media. Thus, opportunities, anticipating threats, and implementing the right strategies can help students increase their chances of becoming sharia bank employees.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".