How to Be Productive in PhD Level: A Needs Assessment Study for Doctoral Students' Research Productivity.
Bibliographic record
Abstract
The aim of the study is to conduct a needs assessment study to determine research productivity needs of doctoral students. A mixed method approach and fully mixed con-current dominant status design is used in the current study. The participants of the study included doctoral students, professors, and deans of (graduate schools) at a university in Turkey from the various Social and Natural Sciences Departments so that the needs of doctoral students from different disciplines could be examined. ‘Needs assessment questionnaires’ were administered to 35 doctoral students, 35 professors, and 4 deans; interviews were conducted with 7 doctoral students, 4 professors and 4 deans to collect the data. Findings of the study showed that academic writing skills were the most frequently mentioned skill that the doctoral students need to improve for their research productivity. In interviews, needs of the participants divided into personal factors like interest and positive attitudes towards research, intrinsic motivation, questioning and writing skills, scientific method and foreign language knowledge; institutional factors like support of advisor/professors, taking part in collaborative and interdisciplinary studies, joining the research projects, easy access of resources, elimination of bureaucratic obstacles, and environmental factors like support of individuals in immediate surroundings of doctoral students.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".