Bibliographic record
Abstract
This paper proposes a research plan to investigate the research methods issues (i.e. research design, sampling methods, data collection methods, data analysis techniques, measurement scales, and reliability/validity tests, among others) used in business students’ thesis/dissertation works in institutions of higher learning. Specifically, the proposed research aims to help in understanding the dominant research methods used by thesis/dissertation research students in the field of business management in institutions of higher learning, shed light on possible relevant research methodology issues in business management education and proffer managerial and theoretical recommendations that will assist research methodology in business disciplines in institutions of higher learning. Among other things, the proposed investigation is expected to help in assessing the quality and relevance of business research works in higher institutions; assist in repositioning business education curricula to align with academic, regulatory and industry expectations; improve the quality and relevance of research works undertaken in business schools in institutions of higher learning; and stimulate research in cognate areas.
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 | 0.492 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.040 | 0.041 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".